Search Results (1,009)

Remote sensing image dehazing remains a formidable challenge due to complex atmospheric scattering and large-scale spatially varying degradation, which severely compromise fine-grained surface details. While recent diffusion-based restoration frameworks, such as DiffIR, have achieved remarkable efficiency by injecting compact diffusion priors into deterministic networks, they typically rely on a monolithic global Image Prior Representation (IPR). However, such a global design is suboptimal for the dehazed results of remote sensing imagery, where haze distribution exhibits strong spatial heterogeneity and scale dependency. To address this limitation, this paper presents the Hierarchical and Scale-Adaptive Diffusion Prior (HS-DiffIR) framework. Specifically, Hierarchical Image Prior Representation decomposes the holistic diffusion latent into multi-scale priors aligned with the hierarchical stages of the restoration network. Such a design facilitates fine-grained, scale-aware guidance by projecting the compact global latent into layer-specific representations, thereby bypassing the computational burden of high-dimensional generative modeling. Complementing this, the Scale-Adaptive Injection mechanism utilizes lightweight learnable coefficients to dynamically modulate the influence of diffusion priors across different feature scales, allowing the network to adaptively balance global semantic consistency and local detail recovery under dense-haze conditions. Evaluations on remote sensing benchmarks confirm that HS-DiffIR generally outperforms the DiffIR baseline. The method yields superior quantitative metrics (particularly PSNR) at a marginal computational cost while demonstrating robust detail restoration in regions subject to severe, spatially variant haze. Full article

Hydrogen is a promising carbon-neutral fuel for future internal combustion engines due to its wide flammability range, high flame speed, and absence of carbon-based emissions. However, its high reactivity significantly increases susceptibility to abnormal combustion phenomena such as knock and pre-ignition, which can compromise engine efficiency, durability, and operational stability. Accurate detection and characterization of knock in hydrogen-fueled spark-ignition engines remain challenging due to the highly transient, broadband, and cycle-dependent nature of abnormal combustion-induced pressure oscillations. Conventional knock indicators based solely on time-domain pressure oscillations or fixed-band frequency analysis are limited in their ability to capture transient resonance behavior and cyclic variability. This study presents an integrated frequency- and time–frequency-domain methodology for knock detection using high-resolution in-cylinder pressure data acquired from a single-cylinder research engine operating under hydrogen port fuel injection (PFI). A discrete Fast Fourier Transform (DFFT) approach applied at stationary points of dynamically windowed pressure signals enables accurate identification of dominant resonance modes while minimizing spectral leakage. A Gaussian-based adaptive windowing strategy is introduced to capture combustion-driven cyclic variations more effectively. Short-Time Fourier Transform (STFT) and sum-based spectral analysis further provide detailed time–frequency localization of transient knock events. The proposed methodology demonstrates a clear separation between normal combustion and knock conditions, enabling reliable cycle-by-cycle identification of abnormal combustion events under varying operating conditions. The experimentally observed resonance frequencies are validated against theoretical predictions using Draper’s acoustic resonance equation, supporting the physical interpretation of knock-induced pressure oscillations. The results demonstrate that the proposed adaptive spectral methodology significantly improves knock detection accuracy compared to conventional indicators and provides a robust framework for advanced knock diagnostics, engine calibration, and combustion control in hydrogen-fueled engines. Full article

Remote sensing change detection is crucial for urban expansion monitoring and ecological assessment. Recently, methods based on Convolutional Neural Networks (CNNs) and Transformers have advanced significantly. However, state-of-the-art models relying primarily on pure spatial-domain modeling and absolute feature differences struggle to balance global semantics with high-frequency boundary details. This paradigm loses physical change directionality and amplifies pseudo-change noise in complex backgrounds. To overcome this, we propose a Frequency-Aware Cross-Layer Change Detection Network (FACDNet) that leverages frequency-spatial synergy to enhance feature discriminability. Specifically, a Wavelet Interaction Block (WIB) decouples bitemporal features using Haar wavelets, employing heterogeneous attention to targetedly reinforce macroscopic semantics and edge textures. Furthermore, to mitigate noise in shallow features, a Cross-Layer Frequency Context Aggregator (CLFCA) injects deep global semantics top-down, purifying multi-scale spatial gating signals. Finally, a Context-guided Difference Fusion Module (CDFM) extracts direction-aware bidirectional difference features, utilizing the purified gating to accurately suppress pseudo-changes. Extensive experiments on the LEVIR-CD and highly challenging SHCD datasets demonstrate FACDNet’s remarkable robustness. It achieves change-class F1-scores of 92.04% and 83.64%, and Intersection over Union (IoU) scores of 85.26% and 71.89%, respectively, achieving highly competitive performance compared with existing mainstream methods. Full article

Object detection in aerial imagery faces extreme target sparsity and high-intensity environmental interference, causing weak targets to be submerged in background clutter. To address this, we propose a Collaborative Feature Purification Detection Transformer (CFP-DETR), which reconstructs discriminative target representations through a collaborative feature purification mechanism. Specifically, the Global Context Denoising Module (GCDM) first suppresses environmental noise at the semantic level to enhance target saliency. The purified features are then fused across scales through an Adaptive Cross-scale Feature Alignment (ACFA) module, which resolves spatial misalignment that otherwise dilutes small-object features during multi-level interaction. Concurrently, a Fine-Grained Detail Injection Module (FGDIM) recovers shallow high-resolution details and injects them into the semantic flow, compensating for information loss caused by progressive downsampling. Together, these modules denoise, align, and recover features to counteract submergence at different stages. Additionally, an efficient lightweight variant, Efficient Lightweight CFP-DETR (EL-CFP-DETR), reconstructs the backbone with partial convolution and structural re-parameterization to improve efficiency while maintaining competitive detection accuracy. Extensive experiments across five datasets validate the effectiveness of this collaborative design. On the SeaDronesSee dataset, CFP-DETR increases $A{P}_{50}$ and $A{P}_S^{val}$ by 1.64% and 4.03% over the baseline, while EL-CFP-DETR reduces parameters by 18% to 16.4M and GFLOPs by 15% to 48.3, reaching 42.8 FPS. Notably, CFP-DETR achieves an inference speed of 37.72 FPS, a 31.2% improvement over the baseline Real-Time Detection Transformer (RT-DETR). Full article

Extracting roads accurately from remote sensing images is important for map updates, traffic analysis, and infrastructure monitoring. Medium-resolution multispectral images can provide useful surface and background information, but when used alone, the spatial details are limited for retaining narrow roads, intersection structures, and fine road topologies. To address this problem, this paper proposes GeoRoad-UPerNet, a Geo-1-centered weakly supervised multispectral framework for road extraction. In this framework, Geo-1 serves as the primary 16-band multispectral source, Sentinel-2 Level-2A imagery serves as auxiliary contextual support, and OpenStreetMap (OSM) road information is converted into proxy supervision rather than dense manual ground truth. GeoRoad-UPerNet contains three modules: a Geo Spectral Semantic Stem (GSSS), a Geo-Auxiliary Gated Fusion module (GAGF), and a Road Semantic Multi-Task Head (RSMH). GSSS strengthens road-sensitive multispectral responses in the Geo-1 branch. GAGF injects Sentinel-2 context through a Geo-centered gate instead of symmetric channel concatenation. RSMH imposes restrained hierarchy- and material-aware semantic regularization on the shared decoder representation during training. On the fixed source-domain benchmark, the complete model achieves an IoU of 0.7204, an F1-score of 0.8375, a Precision of 0.8092, and a Recall of 0.8678 against OSM-derived proxy masks. Relative to the UPerNet-MiT-B3 early-fusion baseline, IoU, F1-score, and Precision increase by 6.29%, 3.65%, and 12.58%, respectively. These results indicate that role-aware multisource organization improves road extraction under proxy supervision and reduces boundary noise and background false positives. Full article

Introduction: Chronic wounds and pathologic scars remain a persistent challenge in plastic surgery. Conventional treatments can be costly and inconsistent, prompting interest in regenerative approaches that utilize autologous tissue. Emulsified fat produces nanofat through mechanical processing and contains adipose-derived stem cells, stromal vascular fractions, extracellular matrix proteins, cytokines and growth factors. The purpose of this systematic review is to evaluate the use of autologous nanofat for wound healing and scar management, with emphasis on preparation techniques, treatment indications, and outcomes. Methods: A comprehensive PubMed search with no date restrictions was conducted in January 2026 using MeSH terms and keywords related to nanofat and wound-healing applications. Studies were screened independently by two reviewers using the Rayyan platform. Eligible studies evaluated nanofat for wound healing in human or animal subjects; non-English articles, studies not involving nanofat, editorials, and conference abstracts were excluded. The extracted data included study characteristics, participant numbers, treatment details, indications, adjunct therapies, follow-up duration, outcomes, and complications. Studies were grouped by clinical application, with individual reports included in multiple categories when relevant. Results: The search identified 53 records, of which 22 studies met the inclusion criteria after screening. These included 20 human and two animal studies spanning randomized controlled trials (n = 3), prospective trials (n = 6), retrospective analyses (n = 6), case series (n = 4), and case reports (n = 3). Mechanical emulsification was the predominant autologous nanofat preparation method (91%), often combined with filtration or centrifugation. Clinical indications in human studies were diverse, most commonly including scar treatment (n = 14) (acne, burns, depressed, and post-surgical), followed by chronic wounds (n = 3) and reconstructive applications (n = 3). Nanofat was administered via injection in 86% of studies (n = 19), typically using fine-gauge needles or microcannulas with intradermal or subdermal placement, while three studies used non-injection approaches such as topical, membrane, or dressing-based delivery. Scar or aesthetic parameters, measured using VSS, POSAS, physician grading, photography, pigmentation analysis, or clinical appearance, were evaluated in 73% of studies (n = 16), and all reported improvement in variables such as pigmentation, pliability, thickness, texture, or overall appearance. Wound-healing endpoints were assessed in 36% (n = 8), with 100% (n = 8) demonstrating accelerated healing, improved epithelialization, or defect closure. Patient-reported outcomes, including satisfaction or quality of life, were measured in 32% (n = 7), and all showed improvement. Objective imaging modalities (e.g., 3D imaging, ultrasound, angiography, digital analysis) were used in 23% (n = 5), each confirming structural or physiologic improvement. Histologic or biomolecular analyses were performed in 27% (n = 6) and uniformly demonstrated regenerative changes, such as increased angiogenesis, collagen remodeling, or growth factor expression. Treatment was well tolerated, with 77% of studies (n = 17) reporting minimal or no complications and only transient mild adverse effects, including mild pain, bruising, erythema, and edema. Conclusions: Current evidence suggests that autologous nanofat is a promising regenerative therapy for wound healing and scar modulation. Across diverse clinical applications, nanofat has been associated with improved tissue quality, enhanced healing, and favorable patient-reported outcomes, with minimal complications. The mechanical processing of autologous tissue may also involve fewer regulatory concerns compared with more extensively manipulated cellular products. Full article

The rapid development and deployment of COVID-19 genetic vaccines have raised significant concerns regarding their safety and potential to trigger immune reactions against self-tissues. This paper provides a comprehensive histopathologically supported analysis of how the synthesis of the vaccine-derived spike protein can trigger such reactions beyond the injection site, characterized by robust immune cell recruitment. We examine these immune responses based on histopathological evidence that delineates a pattern consistent with self-directed immune activity, including vaccine-associated myocarditis. In this regard, we report two representative cases, marked by immune-cell infiltration, triggered by the synthesis of the vaccine-derived spike protein in the myocardium and in the liver, respectively. Additionally, we provide a detailed characterization of the process and the immune cells involved in these reactions, based on histopathological findings. Understanding these mechanisms is essential for accurately assessing the potential implications of these vaccination technologies on human health. By emphasizing the need for further research into the pharmacokinetics and off-target effects of COVID-19 genetic vaccines, this paper aims to deepen our understanding of their safety profiles and inform future vaccine development. Full article

This article presents a comprehensive analytical, numerical, and experimental study of the amplification and detection of weak signals in magnetically coupled electromagnetic systems, using an architecture consisting of three magnetically coupled coils. A rigorous mathematical model of the system is developed, which includes the formulation of the mutual inductance matrix and a state-space representation that captures the dynamic interaction between the coils. It is important to note that the electromagnetic subsystem is linear and that the stochastic resonance effect is achieved by incorporating an external nonlinear bistable element. In this configuration, a weak periodic signal below a threshold is applied to the primary coil, while a controlled source of Gaussian white noise is injected into a secondary coil. A third coil functions as a sensing element, capturing the superimposed magnetic response resulting from coupling effects. The voltage induced in the sensor coil is subsequently processed by a bistable nonlinear element implemented via a Schmitt trigger, which provides the nonlinearity and bistability necessary to enable stochastic resonance and the detection of the weak periodic signal. The conditions of the SR are analyzed in terms of noise intensity, coupling coefficients, and system parameters, highlighting the existence of an optimal noise level that maximizes the signal-to-noise ratio (SNR) at the output. A detailed simulation framework has been developed in MATLAB/Simulink, enabling a systematic exploration of the parameter space and the validation of theoretical predictions. The simulation results are further supported by experimental measurements obtained from a physical prototype, which show agreement with the proposed model. The main contribution of this work lies in demonstrating that magnetically coupled electromagnetic structures can effectively interact with nonlinear bistable elements to exploit stochastic resonance in the detection of weak signals, even when the electromagnetic domain itself remains linear. The results demonstrate that magnetic coupling is an effective mechanism for mediating constructive interactions between noise and weak signals, thereby improving the detection of the latter. These results extend the applicability of stochastic resonance to hybrid electromagnetic systems and demonstrate its relevance in practical applications. Potential applications include ultra-sensitive magnetic detection, low-power signal detection, magnetic transducers, and robust signal recovery in noisy electromagnetic environments, particularly in contexts where conventional linear amplification fails. Full article

Accurate farmland boundary mapping from high-resolution aerial imagery is vital for precision agriculture, yet existing methods struggle with complex geospatial boundaries and texture degradation in fragmented plots. To address irreversible detail loss under downsampling, difficulty in capturing both sharp boundaries and large-scale textures, and weak boundary supervision without extra annotations, we propose PaintingFormer, an enhanced UNet-based segmentation framework. It introduces three targeted innovations: an original feature retention module (OFRM) that injects raw RGB images into the deepest decoder layer to recover lost details; a dual attention–MLP design combining FeaAttention (full-resolution global attention with linear complexity) and TWLK-MLP (cascaded 3 × 3, 5 × 5, and 7 × 7 depthwise separable kernels within an MLP) to capture multi-scale spatial patterns; and a deep edge loss from the encoder’s bottleneck that enforces boundary constraints without manual edge labels. PaintingFormer surpasses mainstream methods, achieving 84.5% mIoU and 91.5% F1 on Vaihingen, 87.3% mIoU on Potsdam, 53.7% on LoveDA, and 84.2% on our private dataset. This work offers an effective solution for fine-grained farmland segmentation, improving boundary accuracy and texture preservation. Full article

Cancer is a leading cause of death in dogs, and incidence rates in dogs exceed those in humans. Current therapeutic options for canine cancer patients remain limited, with most treatments focused on palliative care. Immune checkpoint inhibitors such as anti-PD-1, anti-PD-L1, and anti-CTLA-4 antibodies that have transformed cancer therapy and expanded the therapeutic options in humans could offer the same clinical benefit in canine cancer patients. This study details the engineering and functional characterization of mouse and chimeric mouse–canine anti-canine PD-1 (cPD-1) monoclonal antibodies. We demonstrate that anti-cPD-1 antibodies block the interaction between cPD-1 and its ligand cPD-L1, thereby inhibiting this immune signaling pathway. In a proof-of-concept study in seven companion canine cancer patients, intratumoral therapy with the lead anti-cPD-1 antibody (HugPetmab) was safe, well-tolerated, had no observed adverse events, and showed evidence of tumor control in a subset of injected tumors. These findings support the potential of HugPetmab antibody as an immunotherapeutic option for treating canine cancer patients. Full article

The current study illustrates the modeling of a biocompatible poly γ-glutamic acid (PGA)–chitosan–rGO nanocomposite hydrogel scaffold, which showed a promising novel scaffold for stimulating central nerve regeneration that addresses the shortcomings of recent therapies and improves tissue engineering, controls inflammation, and restores lost functions after spinal cord injuries (SCIs). In the implementation part of the study, the COMSOL program’s top-notch modeling of a detailed investigation of how a scaffold’s in vivo diffusion affects injured neurons. Michaelis–Menten kinetics is used to characterize the enzyme process of releasing the outer covering shell of the scaffold, PGA, from a biomaterial matrix to the nerve cell. Results suggested that the injectable hydrogel scaffold theoretically reduces extracellular glutamate concentrations, presenting a potential mechanism to mitigate localized excitotoxicity. Future in vivo experimental validation is required to determine if this reduction prevents neural cell death Full article

Underwater images often exhibit low contrast and loss of detail due to light scattering and absorption, which poses significant challenges for visual analysis in aquatic environments. Polarization imaging addresses these issues by exploiting the polarization states of light, effectively reducing backscatter and enhancing image contrast. In this paper, we propose a polarization image fusion method guided by information entropy and a hierarchical-adaptive fusion strategy. Local information entropy is first employed to perform multiscale denoising on Degree of Linear Polarization (DOLP) images, enabling adaptive detail reconstruction while distinguishing texture from noise. Subsequently, a hierarchical fusion framework is applied: low-frequency components are enhanced through detail injection, while high-frequency components are fused using a structure-guided mechanism that leverages low-frequency gradient information to generate soft masks for phase-aligned detail integration and edge sharpening. Experiments conducted on self-collected underwater images, two public underwater datasets, and three general-scene datasets demonstrate that the proposed method improves objective metrics, including information entropy, average gradient, and edge strength. Subjective evaluations further confirm its effectiveness in preserving details and adapting to diverse scenes. Furthermore, rigorous ablation studies and runtime analyses demonstrate that the optimized framework achieves a highly favorable balance between robust, artifact-free detail enhancement and computational efficiency. The proposed approach provides a practical solution for underwater image enhancement, with potential applications in target detection and infrastructure inspection. Full article

Weed segmentation is a fundamental task in precision agriculture, essential for targeted intervention and sustainable farming. However, achieving accurate segmentation remains challenging due to the high visual similarity between weeds and crops, as well as the ambiguous, fine-grained boundaries often present in complex field environments. To address this, we present WS-DINO, a novel weed segmentation network built upon the DINOv2 vision foundation model. Our framework introduces two key innovations: (1) a Feature Prior Module that leverages a Canny-guided refinement process to extract and inject fine-grained cues related to weed texture, morphology, and boundaries into specific blocks of the Vision Transformer; and (2) a Spatial Feature Fusion Module that leverages convolutional layers to generate multi-scale spatial features, which are then fused with the semantically rich token features from DINOv2, effectively compensating for the Transformer’s limitations in capturing local spatial details. Comprehensive evaluation on the public PhenoBench dataset shows that WS-DINO achieves an mIoU of 88.67% and outperforms the evaluated benchmark methods. Moreover, on the challenging MotionBlurred dataset, WS-DINO reaches 88.75% mIoU, showing stable performance under motion blur and degraded visual conditions. Full article

Remote sensing change detection (RSCD) is often disturbed by nuisance appearance variations, which can introduce pseudo-changes and degrade the reliability of predicted change masks. Robust change localization therefore requires that such spurious responses be suppressed while the structural integrity of change regions in complex, high-resolution scenes is maintained. We propose TriFusion-CD, a tri-branch framework that fuses complementary sources of information for reliable change localization. The first branch uses MobileSAM to provide global semantic guidance that promotes spatially coherent predictions. The second branch adopts the CLIP-ResNet50 image encoder with a change-aware enhancement module to extract detail-sensitive change features. The third branch performs frequency decomposition and interacts frequency features with CLIP text embeddings via cross-attention, producing a structural–semantic prior to suppress appearance-induced pseudo-changes. We further design a Semantic Attention Fusion Module (SAFM) to inject MobileSAM semantics into CLIP change features through cross-attention with learnable residual scaling. In addition, an Attention-Modulated Decoder (AMD) translates the fused guidance into multi-scale attention maps and performs progressive top-down refinement, extracting more spatially complete change regions. On the challenging SYSU-CD, JL1-CD, and CDD datasets, which exhibit diverse change patterns and frequent appearance-induced pseudo-changes, TriFusion-CD achieves 72.48% IoU/84.04% F1 on SYSU-CD, 66.04% IoU/79.54% F1 on JL1-CD, and 96.41% IoU/98.17% F1 on CDD, demonstrating strong performance. Full article

Injection molding is essential for mass-producing soft contact lenses, yet molding-induced deformation remains a decisive factor for optical quality. This study systematically evaluated the impact of resin mold design factors (optical zone (OZ) radius of curvature and resin mold thickness) and injection molding parameters (holding pressure and injection speed) on the properties of a dry-state lens (dry lens) using an L18 orthogonal array. The results demonstrated that optimizing the resin mold thickness to 0.9 mm reduced astigmatism by approximately 95%, while high holding pressure and low injection speed improved structural stability. Notably, the findings suggest that the refractive power of the dry lens is more strongly governed by macro-level curvature fluctuations and internal stress distributions arising from the resin mold thickness and shape than by the wavefront aberrations of the resin mold itself. Designs with a smaller radius of curvature (R = 6.5 mm) exhibited substantial power deviations of up to +2.8 D, whereas deviations remained within ±0.2 D for designs with a larger radius of curvature (R = 8.5 mm). For high-precision lens manufacturing, it is indispensable to incorporate a resin mold design that accounts for deformations induced during molding. This study provides comprehensive guidelines for achieving high-quality products by detailing the relationship between injection molding and design. Full article