Search Results (247)

On 10 May 2024, a super geomagnetic storm with a minimum Dst index of less than −400 nT occurred. It has attracted a significant amount of attention in the literature. Using total electron content (TEC) observations from a global navigation satellite system (GNSS), in situ electron density data from the Swarm satellite, and corresponding simulations from the thermosphere–ionosphere–electrodynamics general circulation model (TIEGCM), the dynamic poleward shift of the equatorial ionization anomaly (EIA) during the main phase of the super geomagnetic storm has been explored. The results show that the EIA crests moved poleward from ±15° magnetic latitude (MLat) to ±20° MLat at around 19.6 UT, to ±25° MLat at 21.2 UT, and to ±31° MLat at 22.7 UT. This poleward shift was primarily driven by the enhanced eastward electric field, neutral winds, and ambipolar diffusion. Storm-induced meridional winds can move ionospheric plasma upward/downward along geomagnetic field lines, causing the poleward movement of EIA crests, with minor contributions from zonal winds. Ambipolar diffusion contributes/prevents the formation of EIA crests at most EIA latitudes/the equatorward edge. Full article

This study focuses on S32654 super austenitic stainless steel (SASS) and systematically characterizes the morphology of the sigma (σ) phase and the segregation behavior of alloying elements in its as-cast microstructure. High-temperature confocal scanning laser microscopy (HT-CSLM) was employed to investigate the effect of the rare earth element yttrium (Y) on the solidification microstructure and σ phase precipitation behavior of SASS. The results show that the microstructure of SASS consists of austenite dendrites and interdendritic eutectoid structures. The eutectoid structures mainly comprise the σ phase and the γ₂ phase, exhibiting lamellar or honeycomb-like morphologies. Regarding elemental distribution, molybdenum displays a “concave” distribution pattern within the dendrites, with lower concentrations at the center and higher concentrations at the sides; when Mo locally exceeds beyond a certain threshold, it easily induces the formation of eutectoid structures. Mo is the most significant segregating element, with a segregation ratio as high as 1.69. The formation mechanism of the σ phase is attributed to the solid-state phase transformation of austenite (γ → γ₂ + σ). In the late stages of solidification, the concentration of chromium and Mo in the residual liquid phase increases, and due to insufficient diffusion, there are significant compositional differences between the interdendritic regions and the matrix. The enriched Cr and Mo cause the interdendritic austenite to become supersaturated, leading to solid-state phase transformation during subsequent cooling, thereby promoting σ phase precipitation. The overall phase transformation process can be summarized as L → L + γ → γ → γ + γ₂ + σ. Y microalloying has a significant influence on the solidification process. The addition of Y increases the nucleation temperature of austenite, raises nucleation density, and refines the solidification microstructure. However, Y addition also leads to an increased amount of eutectoid structures. This is primarily because Y broadens the solidification temperature range of the alloy and prolongs grain growth perio, which aggravates the microsegregation of elements such as Cr and Mo. Moreover, Y raises the initial precipitation temperature of the σ phase and enhances atomic diffusion during solidification, further promoting σ phase precipitation during the subsequent eutectoid transformation. Full article

This study presents a theoretical model for the thermoelastic response in transmission-mode photoacoustic systems that feature a two-layer structure. The model incorporates volumetric optical absorption in both layers and is based on classical heat conduction theory, hyperbolic generalized heat conduction theory, and fractional heat conduction models including inertial memory in Generalizations of the Cattaneo Equation (GCEI, GCEII, and GCEIII). To validate the model, comparisons were made with the existing literature models. Using the proposed model, the thermoelastic photoacoustic response of a two-layer system composed of a 3D-printed porous polyamide (PA12) substrate coated with a thin, highly absorptive protective dye layer is analyzed. We obtain that the thickness and thermal conduction in properties of the coating are very important in influencing the thermoelastic component and should not be overlooked. Furthermore, the thermoelastic component is affected by the selected fractional model—whether it is subdiffusion or superdiffusion—along with the value of the order of the fractional derivative, as well as the optical absorption coefficient of the layer being investigated. Additionally, it is concluded that the phase has a greater impact than the amplitude when selecting the appropriate theoretical heat conduction model. Full article

Thermochemical nonequilibrium significantly affects the accurate simulation of the aerothermal environment surrounding a hypersonic reentry vehicle entering Earth’s atmosphere during deep space exploration missions. The different heat transfer modes corresponding to each internal energy mode and chemical diffusion have not been sufficiently analyzed. The existing dimensionless correlations for stagnation point aerodynamic heating do not account for thermochemical nonequilibrium effects. This study employs an in-house high-fidelity solver PHAROS (Parallel Hypersonic Aerothermodynamics and Radiation Optimized Solver) to simulate the hypersonic thermochemical nonequilibrium flows over a standard sphere under both super-catalytic and non-catalytic wall conditions. The total stagnation point heat flux and different heating modes, including the translational–rotational, vibrational–electronic, and chemical diffusion heat transfers, are all identified and analyzed. Stagnation point aerodynamic heating correlations have been modified to account for the thermochemical nonequilibrium effects. The results further reveal that translational–rotational and chemical diffusion heat transfers dominate the total aerodynamic heating, while vibrational–electronic heat transfer contributes only about 5%. This study contributes to the understanding of aerodynamic heating principles and thermal protection designs for future hypersonic reentry vehicles. Full article

Single image super-resolution (SISR) aims to reconstruct high-quality images from low-resolution inputs, a persistent challenge in computer vision with critical applications in medical imaging, satellite imagery, and video enhancement. Traditional diffusion model-based (DM-based) methods, while effective in restoring fine details, suffer from computational inefficiency due to their iterative denoising process. To address this, we introduce the Sparse Momentum-based Faster Diffusion Model (SMFDM), designed for rapid and high-fidelity super-resolution. SMFDM integrates a novel encoder state reuse mechanism that selectively omits non-critical time steps during the denoising phase, significantly reducing computational redundancy. Additionally, the model employs a sparse momentum mechanism, enabling robust representation capabilities while utilizing only a fraction of the original model weights. Experiments demonstrate that SMFDM achieves an impressive 71.04% acceleration in the diffusion process, requiring only 15% of the original weights, while maintaining high-quality outputs with effective preservation of image details and textures. Our work highlights the potential of combining sparse learning and efficient sampling strategies to enhance the practical applicability of diffusion models for super-resolution tasks. Full article

Image restoration is an indispensable and challenging task in computer vision, aiming to enhance the quality of images degraded by various forms of degradation. Diffusion models have achieved remarkable progress in AIGC (Artificial Intelligence Generated Content) image generation, and numerous studies have explored their application in image restoration, achieving performance surpassing that of other methods. This paper provides a comprehensive overview of diffusion models for image restoration, starting with an introduction to the background of diffusion models. It summarizes relevant theories and research in utilizing diffusion models for image restoration in recent years, elaborating on six commonly used methods and their unified paradigm. Based on these six categories, this paper classifies restoration tasks into two main areas: image super-resolution reconstruction and frequency-selective image restoration. The frequency-selective image restoration category includes image deblurring, image inpainting, image deraining, image desnowing, image dehazing, image denoising, and low-light enhancement. For each area, this paper delves into the technical principles and modeling strategies. Furthermore, it analyzes the specific characteristics and contributions of the diffusion models employed in each application category. This paper summarizes commonly used datasets and evaluation metrics for these six applications to facilitate comprehensive evaluation of existing methods. Finally, it concludes by identifying the limitations of current research, outlining challenges, and offering perspectives on future applications. Full article

Urban agglomerations serve as essential platforms for regional innovation, while agricultural technology innovation and diffusion play pivotal roles in enhancing agricultural eco-efficiency (AEE). Based on panel data from the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) (2001–2023), this study employs a super-efficiency slacks-based measure model incorporating undesirable outputs to evaluate agricultural eco-efficiency. A modified gravity model is utilized to construct agricultural technology innovation networks (ATINs) in urban agglomerations, and a spatial Durbin model is applied to examine the spillover effects of network structure on eco-efficiency. The results indicate that: (1) Higher-degree centrality within the innovation network significantly improves local agricultural eco-efficiency and produces positive spillover effects on neighboring cities; (2) both direct and spillover effects are significant in central cities, whereas sub-central cities exhibit only a significant direct effect, and peripheral cities display an insignificant direct effect but a significant spillover effect; and (3) enhanced urban informatization, agricultural financial development, and industrial scale substantially strengthen the spatial spillover effects of the innovation network, thereby further advancing agricultural eco-efficiency within the agglomeration. These findings offer theoretical and empirical support for optimizing agricultural technology pathways and enhancing eco-efficiency in urban agglomerations. Full article

This study examines the diffusion of spherical particles in a conical widening channel, with a focus on the effects of deterministic drift and entropic forces. Through numerical simulations, we analyze the motion of particles from a reflecting boundary to an absorbing one. Properties of diffusive motion are explored by inspection of mean squared displacement and mean first passage time. The results show that the diffusion type depends on the drift strength. Without the drift, entropic forces induce effective superdiffusion; however, the increasing drift strength can counterbalance entropic forces and shift the system to standard diffusion and then effective subdiffusion. The mean squared displacement exhibits bending points for high drift values, as predicted by one-dimensional theoretical considerations. The study underscores the importance of considering deterministic and entropic forces in confined geometries. Full article

To address the graphitization of diamond induced by high temperatures during laser cladding of diamond-reinforced composites, this study proposes a laser cladding method utilizing Inconel 718 (IN718) nickel-based alloy as a transition layer which has a lower melting point than the substrate of 45# steel. And then, in order to analyze the detailed characteristics of the samples, scanning electron microscopy (SEM), EDS, Raman spectral analyzer, super-depth-of-field microscope, and friction tests were used. Experimental study and the test results demonstrate that the IN718 transition layer enhances coating performance through dual mechanisms: firstly, its relatively low melting point (1392 °C) reduces the molten pool’s peak temperature, effectively suppressing thermal-induced graphitization of the diamond; on the other hand, simultaneously it acts as a diffusion barrier to inhibit Fe migration from the substrate and weaken Fe–C interfacial catalytic reactions. Microstructural analysis reveals improved diamond encapsulation and reduced interfacial sintering defects in coatings with the transition layer. Tribological tests confirm that samples with the transition layer L exhibit lower friction coefficients and significantly enhanced wear resistance compared to those without. This study elucidates the synergistic mechanism of the transition layer in thermal management optimization and interfacial reaction suppression, providing an innovative solution to overcome the high-temperature damage bottleneck in laser-clad diamond tools. Full article

Facial image super-resolution seeks to reconstruct high-quality details from low-resolution inputs, yet traditional methods, such as interpolation, convolutional neural networks (CNNs), and generative adversarial networks (GANs), often fall short, suffering from insufficient realism, loss of high-frequency details, and training instability. Furthermore, many existing models inadequately incorporate facial structural attributes and semantic information, leading to semantically inconsistent generated images. To overcome these limitations, this study introduces an attribute-prior conditional diffusion implicit model that enhances the controllability of super-resolution generation and improves detail restoration capabilities. Methodologically, the framework consists of four components: a pre-super-resolution module, a facial attribute extraction module, a global feature encoder, and an enhanced conditional diffusion implicit model. Specifically, low-resolution images are subjected to preliminary super-resolution and attribute extraction, followed by adaptive group normalization to integrate feature vectors. Additionally, residual convolutional blocks are incorporated into the diffusion model to utilize attribute priors, complemented by self-attention mechanisms and skip connections to optimize feature transmission. Experiments conducted on the CelebA and FFHQ datasets demonstrate that the proposed model achieves an increase of 2.16 dB in PSNR and 0.08 in SSIM under an 8× magnification factor compared to SR3, with the generated images displaying more realistic textures. Moreover, manual adjustment of attribute vectors allows for directional control over generation outcomes (e.g., modifying facial features or lighting conditions), ensuring alignment with anthropometric characteristics. This research provides a flexible and robust solution for high-fidelity face super-resolution, offering significant advantages in detail preservation and user controllability. Full article

Calcium ion sensors are essential in clinical diagnosis, particularly in the management of chronic kidney disease. Multiple approaches have been developed to measure calcium ions, including flame photometry and ion chromatography. However, these devices are bulky and require specialized staff for operation and evaluation. The integration of all-solid-state ion-selective determination allows the design of miniaturized and low-cost sensing that can be used for the continuous monitoring of electrolytes. However, clinical use has been limited due to the low electrochemical stability and selectivity and high noise rate. This manuscript reports for the first time a novel miniaturized Ca²⁺ ion-selective sensor, developed by using a two-layer nanocomposite thin film (5 µm thick). The device consists of functionalized silica nanoparticles embedded in a poly(vinyl chloride) (PVC) film, which was deposited onto a nanoporous zirconium silicate nanoparticle layer that served as the sensing surface. Systematic evaluation revealed that perfluoroalkane-functionalized silica nanoparticles enhanced Ca²⁺ selectivity by minimizing K⁺ diffusion, confirmed by both potentiometric measurements and quartz microbalance studies. The final sensor demonstrated a super-Nernstian sensitivity of 37 mV/Log[Ca²⁺], a low signal drift of 28 µV/s, a limit of detection of 1 µM, and exceptional selectivity against Na⁺, K⁺, and Mg²⁺ ions. Long-term testing showed stable performance over three months of continuous operation. Clinical testing was conducted on patients with chronic kidney disease. An accurate real-time monitoring of electrolyte dynamics in dialysate samples was observed, where final concentrations matched those observed in physiological conditions. Full article

Land Surface Temperature (LST) is a parameter retrieved through the thermal infrared band of remote sensing satellites, and it is a crucial parameter in various climate and environmental models. Compared to other multispectral bands, the thermal infrared bands have lower spatial resolution, which limits their practical applications. Taking the Heihe River Basin in China as a case study, this research focuses on LST data retrieved from the SDGSAT-1 using the three-channel split-window algorithm. In this paper, we propose a novel approach, the Information-Guided Diffusion Model (IGDM), and apply it to downscale the SDGSAT-1 LST image. The results indicate that the downscaling accuracy of the SDGSAT-1 LST image using the proposed IGDM model outperforms that of Linear, Enhanced Deep Super-Resolution Network (EDSR), Super-Resolution Convolutional Neural Network (SRCNN), Discrete Cosine Transform and Local Spatial Attention (DCTLSA), and Denoising Diffusion Probabilistic Models (DDPM). Specifically, the RMSE of IGDM is reduced by 55.16%, 51.29%, 48.39%, 52.88%, and 17.18%. By incorporating auxiliary information, particularly when using NDVI and NDWI as auxiliary inputs, the performance of the IGDM model is significantly improved. Compared to DDPM, the RMSE of IGDM decreased from 0.666 to 0.574, MAE dropped from 0.517 to 0.376, and PSNR increased from 38.55 to 40.27. Overall, the results highlight the effectiveness of the auxiliary information-guided SDGSAT-1 LST downscaling diffusion model in generating high-resolution remote sensing LST data. Additionally, the study reveals the spatial feature impact of different auxiliary information in LST downscaling and the variations in features across different regions and temperature ranges. Full article

The migration of radioactive waste in geological environments often exhibits anomalies, such as tailing and early arrival. Fractional derivative models (FADE) can provide a good description of these phenomena. However, developing models for solute transport in unsaturated media using fractional derivatives remains an unexplored area. This study developed a variably saturated fractional derivative model combined with different release scenarios, to capture the abnormal increase observed in monitoring wells at a field site. The model can comprehensively simulate the migration of nuclides in the unsaturated zone (impermeable layer)—saturated zone system. This study fully analyzed the penetration of pollutants through the unsaturated zone (retardation stage), and finally the rapid lateral and rapid diffusion of pollutants along the preferential flow channels in the saturated zone. Comparative simulations indicate that the spatial nonlocalities effect of fractured weathered rock affects solute transport much more than the temporal memory effect. Therefore, a spatial fractional derivative model was selected to simulate the super-diffusive behavior in the preferential flow pathways. The overall fitness of the proposed model is good (R² ≈ 1), but the modeling accuracy will be lower with the increased distance from the waste source. The spatial differences between simulated and observed concentrations reflect the model’s limitations in long-distance simulations. Although the model reproduced the overall temporal variation of solute migration, it does not explain all the variability and uncertainty of the specific sites. Based on the sensitivity analysis, the fractional derivative parameters of the unsaturated zone show higher sensitivity than those of the saturated zone. Finally, the advantages and limitations of the fractional derivative model in radionuclide contamination prediction and remediation are discussed. In conclusion, the proposed FADE model coupled with unsaturated and saturated flow conditions, has significant application prospects in simulating nuclide migration in complex geological and hydrological environments. Full article

Image super-resolution (SR) reconstruction is a critical task aimed at enhancing low-quality images to obtain high-quality counterparts. Existing denoising diffusion models have demonstrated commendable performance in handling image SR reconstruction tasks; however, they often require thousands—or even more—diffusion sampling steps, significantly prolonging the training duration for the denoising diffusion model. Conversely, reducing the number of diffusion steps may lead to the loss of intricate texture features in the generated images, resulting in overly smooth outputs despite improving the training efficiency. To address these challenges, we introduce a novel diffusion model named RapidDiff. RapidDiff uses a state-of-the-art conditional noise predictor (CNP) to predict the noise distribution at a level that closely resembles the real noise properties, thereby reducing the problem of high-variance noise produced by U-Net decoders during the noise prediction stage. Additionally, RapidDiff enhances the efficiency of image SR reconstruction by focusing on the residuals between high-resolution (HR) and low-resolution (LR) images. Experimental analyses confirm that our proposed RapidDiff model achieves performance that is either superior or comparable to that of the most advanced models that are currently available, as demonstrated on both the ImageNet dataset and the Alsat-2b dataset. Full article

Conventional neural network-based approaches for single remote sensing image super-resolution (SRSISR) have made remarkable progress. However, the super-resolution outputs produced by these methods often fall short in terms of visual quality. Recent advances in diffusion models for image generation have demonstrated remarkable potential for enhancing the visual content of super-resolved images. Despite this promise, existing large diffusion models are predominantly trained on natural images, which have huge differences in data distribution, making them hard to apply in remote sensing images (RSIs). This disparity poses challenges for directly applying these models to RSIs. Moreover, while diffusion models possess powerful generative capabilities, their output must be carefully controlled to generate accurate details as the objects in RSIs are small and blurry. In this paper, we introduce RSDiffSR, a novel SRSISR method based on a conditional diffusion model. This framework ensures the high-quality super-resolution of RSIs through three key contributions. First, it leverages a large diffusion model as a generative prior, which substantially enhances the visual quality of super-resolved RSIs. Second, it incorporates low-rank adaptation into the diffusion UNet and multi-stage training process to address the domain gap caused by differences in data distributions. Third, an enhanced control mechanism is designed to process the content and edge information of RSIs, providing effective guidance during the diffusion process. Experimental results demonstrate that the proposed RSDiffSR achieves state-of-the-art performance in both quantitative and qualitative evaluations across multiple benchmarks. Full article