Search Results (7,892)

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

Accurate rainfall information is essential for rainfall–runoff modeling in monsoon-influenced basins, where pronounced spatial variability and limited gauge coverage introduce significant uncertainty. Satellite precipitation products provide spatially continuous estimates but are affected by systematic biases, and improvements in statistical rainfall accuracy do not necessarily translate into hydrologically consistent model forcing. This study interpreted satellite rainfall bias correction through a rainfall–runoff framework in the Phetchaburi River Basin, Thailand, using the DWCM-AgWU hydrological model. Simulations were driven by gauge observations and multiple satellite-based rainfall products (GSMaP, CMORPH, CHIRPS, and PERSIANN-CCS), with bias correction applied using Linear Scaling and Quantile Mapping under rainfall-specific calibration. Results showed that bias correction significantly modified rainfall characteristics in distinct ways. Linear Scaling primarily preserved temporal and spatial structure while adjusting rainfall magnitude, whereas Quantile Mapping improved the distributional representation of rainfall intensities. These differences propagated through hydrological processes, leading to systematic variations in runoff responses across multiple metrics, including water balance consistency, peak magnitude, and timing errors. This suggests that each method performs differently depending on the aspect of system response. Rather than identifying a universally optimal method, the findings highlight trade-offs in how rainfall correction strategies influence hydrological system response. Runoff behavior is interpreted as a process-level indicator of rainfall representation, emphasizing that hydrological consistency depends not only on rainfall accuracy but also on its interaction with model structure. These results suggest a process-oriented perspective for interpreting the role of satellite rainfall products in regulated and monsoon-affected basins. Full article

Bovine spongiform encephalopathy (BSE) is a fatal transmissible spongiform encephalopathy caused by the misfolding of the host prion protein (PrP), representing a unique intersection between molecular pathology, neuroanatomy, and public health regulation. Although historically framed as a single feedborne epizootic, BSE is now recognized as a spectrum of strain-defined prion disorders encompassing classical and atypical forms with distinct origins, neuroanatomical trajectories, and surveillance implications. This review integrates advances in prion biology, neurodegenerative mechanisms, and anatomical pathways of neuroinvasion to reframe BSE as a heterogeneous disease entity. We synthesize evidence on PrP^C structure, trafficking, and proteolytic processing to explain how normal cellular physiology enables strain-specific conversion to pathogenic PrP^Sc and subsequent neurotoxicity. Distinct patterns of neuroinvasion and regional vulnerability are discussed for classical versus atypical (H- and L-type) BSE, highlighting differences in lymphoid involvement, brainstem targeting, and cortical or cerebellar tropism. We further examine how these biological differences translate into diagnostic sensitivity, surveillance design, and zoonotic risk assessment. By integrating molecular strain diversity with neuroanatomical connectivity, this review underscores the limitations of obex-centered surveillance for atypical BSE and emphasizes the need for proportionate yet precautionary monitoring strategies. These considerations should be interpreted in light of surveillance-dependent detection biases, which influence the apparent distribution of BSE forms. Ultimately, BSE emerges as a critical model for understanding how protein misfolding disorders bridge cellular mechanisms, animal health, and human public health policy. Full article

To develop secure, fast, and interoperable smart substations, it is vital to understand the current situation and potential future directions of the technologies involved. This study presents the evolution and state of the art of the Generic Object Oriented Substation Event (GOOSE) communication protocol, defined by the International Electrotechnical Commission (IEC) 61850 standard. A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. This included journal articles published from 2004 to 2025 and conference papers from 2020 to 2025, written in English within Engineering. Only studies primarily focusing on GOOSE, citing it at least ten times, and indexed in the Scopus, IEEE Xplore, and Web of Science databases were included. The quantitative analysis used SciMAT software, complemented by a qualitative analysis. Due to the bibliometric and thematic nature of this review, potential biases were considered at the review level rather than by applying a formal study-level risk-of-bias tool. The final analysis comprised 82 journal articles and 84 conference papers. The results offer a comprehensive mapping of GOOSE research evolution, identify nine main challenges and limitations from the last 22 years, and highlight current research directions. The literature reveals methodological heterogeneity, a predominance of simulation-based approaches, and limited large-scale empirical validation. Full article

Vector–matrix multiplication (VMM), implemented through multiply–accumulate (MAC) operations, represents the dominant computational primitive in many artificial intelligence (AI) workloads. When executed on conventional von Neumann architectures, VMM operations suffer from important energy consumption and latency due to the separation between memory and processing units. To overcome these limitations, crossbar arrays built from Resistive Random Access Memory (RRAM) cells have been proposed for accelerating VMM computations. In this work, we investigate the key optimization trade-offs associated with implementing RRAM-based neural networks for classification applications. A simple two-layer neural network is first defined and trained in software to generate the weight matrices and bias parameters. Next, three hardware implementation scenarios are evaluated depending on whether negative floating-point numbers are used: Positive Weights Only (PWO), Positive and Negative Weights Only (PNWO), and Positive and Negative Weights with Biases (PNWB). The different implementations are analyzed at the hardware level by examining classification accuracy, energy efficiency, latency, and area overhead. The study further incorporates important RRAM limitations, including restricted conductance range and device variability. Hardware results show that the PWO scenario offers the lowest energy consumption (189 fJ/MAC) and area overhead but results in the lowest accuracy. PNWO and PNWB significantly improve accuracy (+177% and +180%) but increase energy consumption (+63% and +87%) and area (×2 and ×2.1). Under variability effects, PWO achieves better accuracy (94.65%), followed by PNWO (93.11%) and PNWB (92.11%). Full article

A Principal Component-Based Spectral Standardization (PCSS) method was developed to standardize hyperspectral radiance spectra onto a fixed wavelength grid. This enables the direct comparison of radiance or reflectance spectra across different spatial pixels of an imaging spectrometer or between different instruments. The method was validated using simulated Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) spectra. The PCSS approach demonstrated high accuracy: the average root-mean-square uncertainty across all CPF channels remained below 0.07%, with maximum individual-channel uncertainties under 1%. Compared to methods based on spectral interpolation, PCSS produced significantly lower biases with tighter error distributions, particularly in spectrally rich regions. Measured Hyper Spectral Imager for Climate Science (HySICS) balloon data provided further validation. PCSS successfully estimated wavelength shifts that closely matched measured data, even when utilizing approximated Jacobians, demonstrating the method’s robustness. Because it relies on a pre-computed lookup table for model parameters, PCSS bypasses the need for intensive radiative transfer calculations, making it highly computationally efficient. Beyond CPF, this method can easily be adapted for other hyperspectral sensors by substituting their respective wavelength grids and instrument line shape functions, offering a powerful tool to improve cross-calibration between different satellite sensors. Full article

In this work, a novel understanding of the main failure mechanism of a Schottky p-GaN gate AlGaN/GaN HEMT subject to forward gate stress is reported. First an experimental characterization of the gate leakage current (I_GSS) at different temperatures is reported. Then, Technology Computer Aided Design (TCAD) simulations are used to reproduce the experimental I_GSS thanks to the impact ionization model, also at different temperatures. Simulation results underline how the stressed regions for the Device Under Test (DUT) at high gate biases are the Schottky/p-GaN interface, the p-GaN/AlGaN barrier interface, and p-GaN sidewalls. Moreover, Time Dependent Gate Breakdown (TDGB) measurements were done, and the TEM analysis on the failed device showed the lattice crystal damage located at the p-GaN/AlGaN interface, in accordance with TCAD simulations’ current density distribution at high voltage gate stress. Full article

The burgeoning scale of Pre-trained Large Models (PLMs) has intensified the demand for efficient inference without compromising performance, while existing large model collaborative frameworks have shown promise, they often suffer from functional redundancy among experts and limited robustness in complex cross-domain scenarios. In this paper, we propose Tri-gate Routing for Inference via Decoupled Efficient Network Technologies (TRIDENT), a highly efficient and robust heterogeneous collaborative inference framework. TRIDENT leverages the complementary inductive biases of MLP (for statistical patterns) and KAN (for symbolic logic) to maximize reasoning potential with minimal parametric overhead. To address feature homogenization in traditional distillation, we introduce Orthogonal Feature Decoupling Distillation, utilizing an orthogonality loss ${\mathcal{L}}_{orth}$ for functional decoupling and a reconstruction loss ${\mathcal{L}}_{recon}$ to anchor decoupled features to the PLM knowledge manifold. During inference, a Dual-Threshold Arbiter effectively detects expert hallucinations by integrating individual confidence ${\tau }_{con}$ and heterogeneous consistency ${\tau }_{agree}$. Extensive experiments on CIFAR-100-LT, XNLI, and GSM8K demonstrate that TRIDENT significantly reduces the Invocation Rate (IR) of PLMs while maintaining high accuracy. Our findings reveal a distinct Pareto optimal balance and validate the spontaneous division of labor between heterogeneous experts. By transcending the limitations of single-architecture systems, TRIDENT provides a robust and interpretable pathway for efficient collaborative intelligence. Full article

Images generated by artificial intelligence recombine visual fragments learned from datasets, producing representations based on criteria of semantic proximity and aesthetic familiarity. These images lie in an intermediate zone between verisimilitude and statistical construction, requiring new interpretative skills to understand their nature and limitations. This paper explores the need to develop visual literacy for generative AI, understood as the critical ability to analyse generation processes, recognise implicit biases, and verify the consistency of the representations produced. Through some case studies, prompting is analysed as a dialogical and reflective practice that highlights recurring patterns in datasets and diffusion models. The cases highlight how automatic composition tends to reproduce dominant cultural patterns related to gender, posture, and professional role. This paper introduces NEGOTIA, a seven-step framework designed to foster critical and operational visual literacy, applicable in educational and design contexts where synthetic images function as tools for representation, communication, and verification. NEGOTIA offers a replicable model for education and design practice. Full article

Excessive osteoclast activity drives inflammatory bone loss in osteoporosis, rheumatoid arthritis, and periodontitis. Natural compounds represent promising therapeutic candidates with favorable safety profiles; however, few exhibit pathway-biased mechanisms of action. Here, we report that angelic acid (AA), a naturally occurring unsaturated monocarboxylic acid, potently inhibits RANKL-induced osteoclastogenesis. This effect occurs with an IC₅₀ of 1.9 µM without cytotoxicity. Mechanistically, AA selectively suppressed RANKL-activated phosphorylation of ERK1/2, p38, and JNK (all three MAPK branches), while leaving NF-κB transcriptional activity unaffected. This preferential MAPK suppression disrupted downstream NFATc1 nuclear translocation, thereby preventing NFATc1-driven transcription of osteoclast-specific effector genes including TRAP, cathepsin K, and Atp6v0d2. These findings identify AA as a novel inhibitor of the RANKL–MAPK–NFATc1 axis, providing a mechanistic foundation for its therapeutic development in osteoporosis and other osteolytic diseases. Full article

Background: Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract infections in infants, seniors, and immunocompromised individuals, contributing substantially to the global disease burden. Given the limited preventive options available, developing an effective and safe vaccine remains a public health priority. Methods: An mRNA vaccine encoding the RSV PreF protein was designed and prepared. Antigen properties were evaluated in silico, and the coding sequence was optimized using NLP algorithms. The stability and translational efficiency of the mRNA constructs were verified through in vitro and in vivo assays, followed by immunogenicity evaluation of the formulated mRNA vaccines in a BALB/c mouse model. Results: The optimized mRNA showed predicted improvements in structural stability and a lower free energy state, which were associated with increased translational efficacy in vitro. Correct antigen conformation and retention of key epitopes were confirmed by intracellular staining followed by flow cytometry. A balanced Th1-biased immune response was induced in mice, characterized by high levels of neutralizing antibodies and antigen-specific T-cell immunity, along with enhanced memory T-cell proliferation and differentiation, indicating long-term immunological memory. Conclusions: A novel RSV PreF mRNA vaccine was successfully developed via optimization of protein structure and mRNA sequence. Superior immunogenicity was demonstrated in the BALB/c mouse model, together with promising potential in terms of vaccine safety and immunological persistence. These findings represent a promising step forward in the pursuit of an effective RSV vaccine and suggest the potential of the developed mRNA vaccine to induce substantial immune responses that may correlate with protection in future challenge studies. Full article

Inferring demographic history from whole-genome data is a central objective in evolutionary and conservation genomics. However, the Pairwise Sequentially Markovian Coalescent (PSMC) framework, one of the most widely used demographic inference methods for whole-genome sequence data, is highly sensitive to sequencing coverage, with low coverage producing systematic underestimation of heterozygosity, which biases effective population size trajectories. Here, we present PSMC-FAC, an automated method designed to optimize false-negative rate correction in low-coverage genomes by minimizing geometric distances between FNR-corrected low-coverage trajectories and their corresponding high-coverage references. Whole-genome datasets from humans, gray wolves, and cattle were downsampled across multiple coverage levels and processed through standard demographic inference pipelines. Corrected trajectories, projected onto a common temporal grid, were compared using Hausdorff and discrete Fréchet distance metrics and optimal correction factors were modeled as a function of sequencing depth using second-degree polynomial regression. Across species and demographic contexts, PSMC-FAC substantially improved concordance between low- and high-coverage trajectories and revealed highly predictable coverage-dependent correction patterns. Overall, PSMC-FAC provides a reproducible and mathematically grounded alternative to subjective correction approaches, enabling reliable demographic inference from moderate-coverage genomes and facilitating broader population-scale genomic analyses. Full article

Accurately characterizing the spatiotemporal variations in precipitation in China’s drylands is important for solving water scarcity in the region, guaranteeing security in the ecological environment, and conducting precise drought disaster management. To reduce the uncertainty in the existing precipitation products, we developed a two-stage machine-learning framework combining extreme gradient boosting (XGBoost) and random forest (RF) residual corrections. Based on the ground-based observation data from 1030 meteorological stations and numerous high-precision precipitation products (GPM IMERG Final V6, MSWEP V2, CMFD 2.0, TerraClimate), a monthly fused precipitation dataset (XGB-RF) for China’s drylands was produced during the 2001–2020 period at the 0.1° resolution. The validation results showed that the XGB-RF had a monthly Kling–Gupta Efficiency (KGE) of 0.941, and it improved 20.6–62.2% relatively with that of input individual products. For the dataset as a whole, we found very consistent, reliable performance in all seasons and topography, in particular in winter time and data-scarce western areas where individual products have large biases. More importantly, the XGB-RF was employed for drought monitoring based on the 1-month Standardized Precipitation Index that calculated the median KGE of 0.888, which made good drought trend tracking and drought features possible. Notably, the KGE for the mean drought intensity was 0.757, which was higher than that of independent original products. This study provides a high-resolution precipitation forcing dataset and demonstrates the effectiveness of two-stage machine learning strategies in enhancing hydroclimatic monitoring and drought risk assessment in arid and semi-arid regions. Full article

The extreme volatility of European energy markets in 2022 has exposed the limitations of conventional forecasting models, necessitating more robust architectures capable of handling non-linear price shocks. This study proposes a novel multi-parallel hybrid forecasting framework that integrates seven heterogeneous neural networks to predict day-ahead electricity prices. The architecture employs a hierarchical approach where six parallel base models (NN1–NN6) feed into a meta-network (NN7) to generate baseline forecasts. To further enhance predictive fidelity, these results undergo a calibration stage using probabilistic error distribution analysis to produce final probability-adjusted forecasts. The model was validated using the Lithuanian electricity market during the highly volatile period of 2020–2022. Empirical results demonstrate a clear “stacking effect,” where the incremental integration of base networks consistently reduces forecasting residuals. The final probability-adjusted configuration achieved a notable nMAE of 1.57% and a sMAPE of 34.25%, significantly outperforming baseline ensemble outputs and state-of-the-art benchmarks reported in recent literature. Specifically, the probability-based refinement proved highly effective in mitigating systematic biases during nighttime and early morning hours, confirming the model’s capacity to maintain accuracy under extreme market stress. Full article

Spaceborne hyperspectral imaging spectrometers enable refined retrieval and quantification of methane point-source emissions. However, the conventional matched filter (MF) systematically underestimates methane enhancements under high-concentration conditions and remains sensitive to spectral inconsistencies across varying observation scenarios. To address these limitations, we improve MF-based retrieval from two aspects: the observation model and the unit absorption spectrum (UAS) representation. First, a Levenberg–Marquardt matched filter (LMMF) is developed by extending the MF framework to a nonlinear retrieval formulation while retaining its data-driven and background-statistics-based characteristics. Specifically, the exponential absorption term is preserved, and methane enhancement is iteratively solved in the nonlinear domain, enabling a more physically consistent retrieval without requiring precise external prior knowledge. Building upon this framework, a spectrally corrected LMMF (SC-LMMF) is further proposed by introducing a lookup-table-based dynamic UAS correction to account for variations in observation geometry, surface elevation, and atmospheric state. Comprehensive validation using idealized and noise-perturbed simulations, end-to-end simulations, and controlled-release experiments demonstrates that the LMMF mitigates high-concentration underestimation relative to the MF. The SC-LMMF further reduces cross-scene systematic biases, shifting retrievals toward a near 1:1 relationship. In controlled-release experiments, the SC-LMMF increased the coefficient of determination (R²) by approximately 50% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by approximately 70% relative to the MF. Overall, the proposed framework enhances the robustness and quantitative consistency of methane point-source retrievals across multisource hyperspectral satellite observations. Full article