Search Results (298)

In this research, an innovative scheme to generate heterogeneous acoustofluidic distributions in various pseudo-Sierpiński-carpet-shaped chambers with different filling fractions and cross-sectional configurations has been proposed and calculated for topographical manipulation of large-scale micro-particles. All of the structural components positioned in the pseudo-fractal chambers are symmetrically distributed in space, and all ultrasonic radiation surfaces hold the unified settings of input frequency point, oscillation amplitude, and initial phase distribution along their respective normal directions. A large number of fascinating acoustofluidic patterns can be generated in the originally-static pseudo-Sierpiński-carpet-shaped chambers at different recursion levels without complicated vibration parameter modulation. The simulation results of acoustofluidic distributions and particle motion trajectories under different radiation surface arrangements further demonstrate the manipulation performance of these specially designed devices, and indicate that controllable spatial partitioning and intensity modulation of the acoustofluidic field can be achieved by adjusting the hierarchical order, cross-sectional configuration and combination mode of the radiation surfaces. Unlike the existing device construction method of miniaturized microfluidic systems, the artificial introduction of fractal elements like Sierpiński carpet/triangle, Koch snowflake, Mandelbrot set, Pythagoras tree, etc., can provide extraordinary perspectives and expand the application range of the acoustofluidic effect, which also makes ultrasonic micro/nano-scale manipulation technology more abundant and diversified. This exploratory research indicates the potential possibility of applying fractal structures as alternative component parts to purposefully customize acoustofluidic distributions for the further research of patterned manipulation of bio-organisms and navigation of micro-robot swarms in brand new ways that cannot be achieved through traditional methods. Full article

Optical and synthetic aperture radar (SAR) imagery are highly complementary in terms of texture details and structural scattering characterization. However, their imaging mechanisms and statistical distributions differ substantially. In particular, pseudo-high-frequency components introduced by SAR coherent speckle can be easily entangled with genuine optical edges, leading to texture mismatch, structural drift, and noise diffusion. To address these issues, we propose WEMFusion, a wavelet-prior-driven framework for frequency-domain decoupling and discrepancy-aware state-space fusion. Specifically, a multi-scale discrete wavelet transform ($DWT$) explicitly decomposes the inputs into low-frequency structural components and directional high-frequency sub-bands, providing an interpretable frequency-domain constraint for cross-modality alignment. We design a hybrid-modality enhancement (HME) module: in the high-frequency branch, it effectively injects optical edges and directional textures while suppressing the propagation of pseudo-high-frequency artifacts, and in the low-frequency branch, it reinforces global structural consistency and prevents speckle perturbations from leaking into the structural component, thereby mitigating structural drift. Furthermore, we introduce a discrepancy-aware gated Mamba fusion (DAG-MF) block, which generates dynamic gates from modality differences and complementary responses to modulate the parameters of a directionally scanned two-dimensional state-space model, so that long-range dependency modeling focuses on discrepant regions while preserving directional coherence. Extensive quantitative evaluations and qualitative comparisons demonstrate that WEMFusion consistently improves structural fidelity and edge detail preservation across multiple optical–SAR datasets, achieving superior fusion quality with lower computational overhead. Full article

This work presents a new and efficient numerical framework for solving three-dimensional time-fractional diffusion and mobile–immobile equations in the Caputo sense. The method is formulated using four-variable Jacobi polynomials, constructed systematically via the Kronecker product of one-dimensional Jacobi bases to accurately represent the multidimensional nature of the governing equations. Within a pseudo-operational collocation formulation, these polynomials enable a highly accurate and computationally efficient approximation of the fractional operators in both temporal and spatial directions. From the theoretical standpoint, the existence and uniqueness of the approximate solution are rigorously established through Schauder’s fixed-point theorem. Furthermore, the Ulam–Hyers stability of the numerical solution is verified, demonstrating the robustness of the method with respect to perturbations in the input data. To reinforce the reliability of the approach, an explicit error bound for the residual function is derived in a Jacobi-weighted Sobolev space, offering a firm analytical basis for assessing convergence. Numerical experiments confirm that the proposed approach achieves superior accuracy and efficiency, highlighting its potential as a powerful tool for high-dimensional fractional partial differential equations. Full article

Dry gas reservoirs play a pivotal transitional role in meeting the net-zero target worldwide. Accurate modelling and simulation of this energy source require fast and reliable prediction of the gas compressibility factor (Z-factor). The experimental measurements of Z-factor are the most reliable source; however, they are expensive and time-consuming. This makes developing accurate predictive models essential. Traditional methods, such as empirical correlations and Equations of States (EoSs), often lack accuracy and computational efficiency. This study aims to address these limitations by leveraging the predictive power of machine learning (ML) techniques. Hence in this study three ML models of Artificial Neural Network (ANN), Group Method of Data Handling (GMDH), and Genetic Programming (GP) were developed. These models were trained on a comprehensive dataset comprising 1079 samples where pseudo-reduced pressure (Ppr) and pseudo-reduced temperature (Tpr) served as input and experimentally measured Z-factors as output. The performance of the developed ML models was benchmarked against two cubic EoSs of Peng–Robinson (PR) and van der Waals (vdW), and two semi-empirical correlations of Dranchuk-Abou-Kassem (DAK) and Hall and Yarborough (HY), and recent developed ML based models, using statistical metrics of Mean Squared Error (MSE), coefficient of determination (R²), and Average Absolute Relative Deviation Percentage (AARD%). The proposed ANN model reduces average prediction error by approximately 70% relative to the PR equation of state and by over 35% compared with the DAK correlation, while maintaining robust performance across the full Ppr and Tpr of dry gas systems. Additionally paired t-tests and Wilcoxon signed-rank tests performed on the ML results confirmed that the ANN model achieved statistically significant improvements over the other models. Moreover, two physical equations using the white-box models of GMDH and GP were proposed as a function of Ppr and Tpr for prediction of the dry gas Z-factor. The sensitivity analysis of the data shows that the Ppr has the highest positive effect of 88% on Z-factor while Tpr has a moderate effect of 12%. This study presents the first unified, statistically validated comparison of ANN, GMDH, and GP models for accurate and interpretable Z-factor prediction. The developed models can be used as an alternative tool to bridge the limitation of cubic EoSs and limited accuracy and applicability of empirical models. Full article

Coaxial stacking between adjacent stems is a key tertiary interaction that defines the spatial organization of RNA junctions, which are core structural motifs in folded RNAs. The accurate prediction of coaxial stacking is critical for RNA 3D structure modeling, yet existing computational tools remain limited, especially for junctions with variable numbers of branches or complex topologies. Here, we present gCoSRNA, a generalizable computational framework for predicting coaxial stacking configurations using RNA sequence and secondary structure as input. Instead of developing separate models for each junction type, gCoSRNA decomposes multi-way junctions into all possible adjacent stem pairs, termed pseudo two-way junctions, and uses a unified RF classifier to evaluate stacking probabilities. Global stacking configurations are inferred by integrating these pairwise predictions, eliminating the need for explicit junction type classification. Benchmarking on two independent test sets (297 RNA junctions), including CASP15/16 and RNA-Puzzles targets, shows that gCoSRNA achieves consistently high accuracy (mean ~0.89) across junctions with two to seven branches, outperforming existing junction-specific methods. These results highlight the model’s ability to capture higher-order structural features and its potential utility in RNA tertiary structure prediction pipelines. Full article

This study applies a modality-bridging framework to automatically construct Chain-of-Thought (CoT) reasoning from meteorological images, reducing the need for expert annotation. The proposed pipeline integrates semantic extraction, Pseudo-CoT generation, and logical fusion to produce structured reasoning chains. Using the WeatherQA benchmark, we build datasets under single-image, 3-image, and 20-image settings—with automated and Expert-Guided variants—and evaluate performance on Areas Affected and Conditional Concern tasks. The results show near-expert spatial reasoning and more compact, well-aligned CoTs with reduced-image inputs. Multi-image settings reveal challenges in integrating dense visual cues, while semantic classification remains difficult due to label ambiguity. Overall, modality-bridging offers a scalable, interpretable, and low-cost approach for multimodal meteorological reasoning. Full article

High accuracy and time synchronous aerosol optical depth (AOD) is essential for atmospheric correction (AC) of medium and high spatial resolution (MHSR) remote sensing data. However, existing high-resolution AOD retrieval methods often rely on sparsely distributed ground-based measurements, which limits their capacity to resolve fine-scale spatial heterogeneity and consequently constrains retrieval performance. To address this limitation, we propose a framework that takes GF-1 top-of-atmosphere (TOA) reflectance as input, where the model is first pre-trained using MCD19A2 as Pseudo-labels, with high-confidence samples weighted according to their spatial consistency and temporal stability, and then fine-tuned using Aerosol Robotic Network (AERONET) observations. This approach enables improved retrieval accuracy while better capturing surface variability. Validation across multiple regions demonstrates strong agreement with AOD measurements, achieving the correlation coefficient (R) of 0.941 and RMSE of 0.113. Compared to models without pretraining, the proportion of AOD retrievals within EE improves by 13%. While applied to AC, the corrected surface reflectance also shows strong consistency with in situ observations (R > 0.93, RMSE < 0.04). The proposed Trans-AODnet significantly enhances the accuracy and reliability of AOD inputs for AC of high-resolution wide-field sensors (e.g., GF-WFV), offering robust support for regional environmental monitoring and exhibiting strong potential for broader remote sensing applications. Full article

Amorphous indium-gallium-zinc-oxide (a-IGZO) thin-film transistors (TFTs) have emerged as promising candidates for next-generation large-area and low-power electronics due to their high mobility, low leakage current, and compatibility with low-temperature fabrication on flexible or transparent substrates. In this work, we report the fabrication of bottom-gate a-IGZO NMOS TFTs using HfO₂ as high-k gate dielectric and Mo top contacts. The devices were electrically characterized through capacitance–voltage (C–V) and current–voltage (I–V) measurements, from which key parameters were extracted. Based on these transistors, we designed, fabricated, and characterized inverters employing four different load configurations: resistive, diode, depletion, and pseudo-CMOS. A comparative analysis was performed in terms of voltage transfer characteristics (VTCs), gain, and noise margins, highlighting that depletion-load inverters offer the highest gain and robust noise margins. Finally, a two-channel multiplexer was designed and fabricated. The multiplexer was characterized under both square and sinusoidal input signals up to 1 kHz, demonstrating correct channel selection and robust switching behavior. These results confirm the potential of a-IGZO TFT-based circuits as building blocks for low-power and high-reliability digital and mixed-signal electronics. Full article

Landslides are geological hazards that endanger socioeconomic development and ecological security, with landslide susceptibility mapping (LSM) playing a critical role in risk management and spatial planning. Recently, ensemble learning (EL) models have gained attention for effectively addressing the limitations of individual deep learning (DL) models in LSM. However, EL models always built on single-pixel, multi-factor inputs struggle to capture the spatial structure features of terrain units, limiting their ability to depict complex disaster patterns. Moreover, the scarcity of landslide samples and high annotation costs constrain model performance in LSM. To overcome these challenges, we propose a Stacking model based on multidimensional feature collaboration and pseudo-labeling techniques, referred to as MFP_Stacking. A stacking EL model is first employed in MFP_Stacking to integrate global statistical attribute features extracted from one-dimensional vectors with multi-scale spatial topological features derived from three-dimensional vectors. This strategy of multidimensional feature collaborative modeling enhances the model’s ability to learn complex environmental patterns associated with landslides. Subsequently, pseudo-labeling techniques are adopted to incorporate unlabeled data into auxiliary training, thereby addressing the problem of sample scarcity. MFP_Stacking was applied to LSM in the Zigui–Badong section of the Yangtze River Basin and in Ya’an City, Sichuan Province. Experimental results demonstrate that the proposed model performs well in overcoming limitations in feature representation, alleviating sample scarcity, and enhancing the quality of LSM outcomes. It achieved an average improvement of 2.4% for the Zigui–Badong section and 2% for Ya’an City across various evaluation metrics compared to other models. Full article

Fuel cells offer a promising route to eliminating in-flight emissions from regional aviation, but certification requires proof that stacks can withstand the vibration and shock environment of turboprop aircraft. As part of the EU-funded NEWBORN project, we combined detailed finite element modeling with shaker tests to evaluate the vibration immunity of PowerCell Group’s prototype stack. The numerical model combined an orthotropic, two-zone 3D mesh of the cell package with reduced-order representations of plates, compression bands, disc springs and the mounting cage. The assembled stack was excited between 10 and 300 Hz using pseudo-random and sine-sweep inputs up to 2.0 g, from which 54 frequency response functions were obtained. The tuned model accurately reproduced the first global modes and captured the overall dynamic behavior with good, though not perfect, agreement. The combined numerical–experimental methodology therefore offers a framework for refining test campaigns and delivering early, qualitative evidence of vibration immunity in fuel cell stacks destined for flight. Full article

An early and accurate detection of retinal lesions is imperative to intercept the course of sight-threatening ailments, such as Diabetic Retinopathy (DR) or Age-related Macular Degeneration (AMD). Manual expert annotation of all such lesions would take a long time and would be subject to interobserver tendencies, especially in large screening projects. This work introduces an end-to-end deep learning pipeline for automated retinal lesion segmentation, tailored to datasets without available expert pixel-level reference annotations. The approach is specifically designed for our needs. A novel multi-stage automated ground truth mask generation method, based on colour space analysis, entropy filtering and morphological operations, and creating reliable pseudo-labels from raw retinal images. These pseudo-labels then serve as the training input for a U-Net architecture, a convolutional encoder–decoder architecture for biomedical image segmentation. To address the inherent class imbalance often encountered in medical imaging, we employ and thoroughly evaluate a novel hybrid loss function combining Focal Loss and Dice Loss. The proposed pipeline was rigorously evaluated on the ‘Eye Image Dataset’ from Kaggle, achieving a state-of-the-art segmentation performance with a Dice Similarity Coefficient of 0.932, Intersection over Union (IoU) of 0.865, Precision of 0.913, and Recall of 0.897. This work demonstrates the feasibility of achieving high-quality retinal lesion segmentation even in resource-constrained environments where extensive expert annotations are unavailable, thus paving the way for more accessible and scalable ophthalmological diagnostic tools. Full article

This study develops an updated national Social Accounting Matrix (SAM) for Greece, based on the 2020 Input–Output Table that captures post-crisis structural and macroeconomic transformations, implemented in Python 3, hence producing a reusable, modular code. This methodological approach facilitates multiplier-based policy analysis of how shocks propagate through the Greek economy, and therefore, this study contributes to the literature by addressing the gap in multiplier analysis for this setting. Output, value-added, and income multipliers were estimated using the Moore–Penrose pseudo-inverse via Singular Value Decomposition (SVD). Findings highlighted the substantial role of government transfers in supporting household and firm incomes, largely due to COVID-19 relief measures. This analysis showed that production expansion in energy, construction, and wholesale and retail trade can stimulate broad economic activity, while service-related sectors play a critical role in income generation and equity considerations. At the same time, firms in trade, hospitality, and real estate were heavily affected by the pandemic shock. The findings of this study provide a benchmark for understanding Greece’s economic structure at a critical moment in time (the COVID-19 pandemic). Full article

The correct assessment of slope stability under seismic loading requires not only the magnitude of ground acceleration to be considered but also its frequency content. In this study, a hybrid finite element/limit equilibrium (FEM–LEM) approach is used to quantify how the dominant frequency of harmonic ground motion affects the dynamic factor of safety, FSdyn, of a large homogeneous slope. Dynamic stresses are computed in QUAKE/W and transferred to SLOPE/W, where a FS calculation is performed at each time step to obtain FS_dyn(t). A design-of-experiment framework is applied to explore combinations of peak ground acceleration and dominant frequency. The results show that FS_dyn is much more sensitive to dominant frequency than to acceleration amplitude within the analyzed ranges, with the strongest reduction in stability occurring with the low input frequencies. Comparison with conventional pseudo-static analysis demonstrates that pseudo-static factors of safety can significantly overestimate stability at low dominant frequencies, and frequency thresholds are identified above which pseudo-static results become closer to the hybrid solution for the studied configuration. Although the model is intentionally simplified (homogeneous, drained conditions and single-frequency excitation), the findings highlight that dominant frequency is a decisive control parameter and should not be neglected in the seismic assessment of large earth structures. Full article

3D Gaussian Splatting (3DGS) is a multi-view 3D reconstruction method that relies solely on image loss for supervision, lacking explicit constraints on the geometric consistency of the rendering model. It uses a multi-view scene-by-scene training paradigm, which limits generalization to unknown scenes in the case of single-view limited input. To address these issues, this paper proposes a Geometric Consistency-High Generalization (GC-HG), a single-view 3DGS reconstruction framework integrating depth prior and a pseudo-triplane. First, we utilize the VGGT 3D geometry pre-trained model to derive depth prior, back-projecting them into point clouds to construct a dual-modal input alongside the image. Second, we introduce a pseudo-triplane mechanism with a learnable Z-plane token for feature decoupling and pseudo-triplane feature fusion, thereby enhancing geometry perception and consistency. Finally, we integrate a parent–child hierarchical Gaussian renderer into the feed-forward 3DGS framework, combining depth and 3D offsets to model depth and geometry information, while mapping parent and child Gaussians into a linear structure through an MLP. Evaluations on the RealEstate10K dataset validate our approach, demonstrating improvements in geometric modeling and generalization for single-view reconstruction. Our method improves Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) metrics, demonstrating its advantages in geometric consistency modeling and cross-scene generalization. Full article

To address the limitations of unimodal visual detection in complex scenarios involving low illumination, occlusion, and texture-sparse environments, this paper proposes an improved YOLOv11-based dual-branch RGB-D fusion framework. The symmetric architecture processes RGB images and depth maps in parallel, integrating a Dual-Encoder Cross-Attention (DECA) module for cross-modal feature weighting and a Dual-Encoder Feature Aggregation (DEPA) module for hierarchical fusion—where the RGB branch captures texture semantics while the depth branch extracts geometric priors. To comprehensively validate the effectiveness and generalization capability of the proposed framework, we designed a multi-stage evaluation strategy leveraging complementary benchmark datasets. On the M³FD dataset, the model was evaluated under both RGB-depth and RGB-infrared configurations to verify core fusion performance and extensibility to diverse modalities. Additionally, the VOC2007 dataset was augmented with pseudo-depth maps generated by Depth Anything, assessing adaptability under monocular input constraints. Experimental results demonstrate that our method achieves mAP50 scores of 82.59% on VOC2007 and 81.14% on M3FD in RGB-infrared mode, outperforming the baseline YOLOv11 by 5.06% and 9.15%, respectively. Notably, in the RGB-depth configuration on M³FD, the model attains a mAP50 of 77.37% with precision of 88.91%, highlighting its robustness in geometric-aware detection tasks. Ablation studies confirm the critical roles of the Dynamic Branch Enhancement (DBE) module in adaptive feature calibration and the Dual-Encoder Attention (DEA) mechanism in multi-scale fusion, significantly enhancing detection stability under challenging conditions. With only 2.47M parameters, the framework provides an efficient and scalable solution for high-precision spatial perception in autonomous driving and robotics applications. Full article