Search Results (157)

Haze commonly degrades visible-spectrum remote sensing (RS) images by reducing contrast and distorting colors. Existing RS dehazing methods still face two limitations. Prior-driven methods rely on handcrafted assumptions that may become unreliable in complex wide-area scenes without explicit sky regions. Learning-based methods require paired training data, yet real aligned hazy/haze-free RS image pairs are difficult to collect, which limits their real-world generalization. To address these limitations, we propose a method called Remote Sensing Image Dehazing via RGB-Space Physical Constraints (RDPC). The new method revisits the atmospheric scattering model (ASM) from the perspective of RS imaging and builds the restoration process on several physical properties of hazy image formation. For atmospheric light estimation, the RGB-space line-convergence behavior of local regions with similar reflectance and slight depth variations is exploited, allowing atmospheric light to be estimated without explicit sky areas. For transmission estimation, the geometric relation between observed pixels and atmospheric light is used in RGB space, where local perpendicularity provides physically plausible haze-removal guidance and global compensation helps avoid excessive darkening and color degradation. The estimated transmission and albedo guidance are further refined by enforcing ASM consistency and variation sparsity through joint optimization. Experiments on synthetic and real-world RS image dehazing benchmarks demonstrate that RDPC achieves competitive performance against representative prior-based and learning-based methods, including Image Dehazing and Exposure (IDE), Iterative Predictor-Critic (IPC), Curvature-to-Plane Prior (C2P), Adaptive Structure-Texture Awareness (ASTA), Asymmetric U-Net (AU-Net), Efficient Multi-scale Prior Fusion (EMPF), and Lightweight Feature Dehazing (LFD), in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), neural image assessment (NIMA), and processing time. Full article

Surface-enhanced Raman scattering (SERS) offers a powerful route for biomolecular detection because it combines molecular specificity with high sensitivity, rapid optical readout, and multiplexing capability. In real biological samples, however, analytical performance is rarely determined by signal enhancement alone. Biofluids such as serum, plasma, saliva, urine, and interstitial fluid contain complex biomolecular mixtures that interfere with target capture, spectral response, and data interpretation. A practical SERS biosensor must therefore localize targets, stabilize spectral responses, tolerate matrix-induced variation, and convert complex spectra into reliable analytical information. This review discusses recent progress in SERS biosensing from an integrated system perspective, with particular focus on artificial intelligence/machine learning (AI/ML)-assisted interpretation. Direct label-free SERS provides chemically transparent readouts but is limited by stochastic adsorption, hotspot heterogeneity, and spectral variation in complex samples. Bio-recognition interfaces improve target localization, while signal-transduction strategies based on nanotags, immunoassays, clustered regularly interspaced short palindromic repeats (CRISPR) systems, nanozymes, and lateral-flow formats decouple molecular recognition from spectral generation. Digital SERS further improves measurement robustness by converting fluctuating intensities into countable, event-based outputs. AI/ML-assisted analysis can support full-spectrum classification, calibration transfer, explainability, and patient-level decision-making. We frame AI/ML-assisted SERS biosensing as an integrated architecture connecting substrate design, interface engineering, signal transduction, digital measurement, and clinical validation. Future progress will depend as much on validation-ready workflows as on plasmonic enhancement itself, especially for systems intended to operate across different samples, instruments, and clinical settings. Full article

Single image dehazing remains a fundamental challenge in computer vision due to the ill-posed nature of the inverse problem and the spatial heterogeneity of real atmospheric haze. Existing convolutional approaches suffer from two structural deficiencies: bounded receptive fields that fail to model large-scale haze gradients, and isotropic kernels insensitive to the directional patterns of atmospheric scattering. This paper proposes ACTA-AOD, a lightweight end-to-end dehazing network that addresses both limitations within a unified framework built upon the AOD-Net K-parameterization. The network integrates two complementary modules: (1) W-EMSAv2, a windowed efficient multi-scale attention module that reduces attention complexity from $\mathcal{O}(N^{2}C)$ to $\mathcal{O}(N{M}^{2}C/4)$ while preserving full-spectrum spatial information through pixel-shuffle reconstruction; and (2) the ACTA Fusion module, which combines structural-reparameterization-based asymmetric convolution with cross-dimensional Triple Attention for direction-sensitive local detail recovery at zero inference-time overhead. On the RESIDE benchmark, ACTA-AOD achieves peak signal-to-noise ratio (PSNR) of 26.02 dB and structural similarity index measure (SSIM) of 0.910 on indoor synthetic data, and 26.13 dB/0.910 on outdoor synthetic data, surpassing the AOD-Net baseline by $+3.41$ dB (indoor) and $+3.58$ dB (outdoor) in PSNR, and exceeding the strongest learning-based baseline (AECRNet, CVPR 2021) by $+1.17$ dB (indoor) and $+1.75$ dB (outdoor). The model processes images at 81 frames per second on a single GPU. Ablation studies and stratified robustness evaluation across five haze density levels confirm the complementary, synergistic contribution of each module. Full article

Chloramphenicol is a broad-spectrum antimicrobial agent whose use in food-producing animals is prohibited in many countries due to its association with severe adverse effects, including idiosyncratic aplastic anemia and genotoxicity. Despite these restrictions, chloramphenicol residues continue to be detected in food products, environmental compartments, and biological matrices, highlighting the need for reliable and sensitive analytical monitoring. This review provides a comprehensive overview of current analytical strategies for the detection of drugs in food and environmental samples, covering screening and confirmatory techniques, sample preparation approaches, and regulatory aspects. Rapid screening methods, such as enzyme-linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs), and biosensors based on antibodies, aptamers, and molecularly imprinted polymers, enable fast and cost-effective preliminary detection. Recent advances in nanomaterials and signal amplification strategies, including fluorescent reporters and surface-enhanced Raman scattering (SERS), have significantly improved sensitivity and assay performance. However, confirmatory methods based on liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) remain the reference standard due to their superior selectivity, sensitivity, and quantitative reliability. Attention is given to sample preparation workflows, including QuEChERS-based protocols and microextraction techniques, which enable efficient analysis of complex matrices. Finally, current regulatory frameworks and analytical challenges related to zero-tolerance policies are discussed, emphasizing the importance of robust and validated analytical methods for effective monitoring and food safety assurance. Full article

The simulation of synthetic aperture radar (SAR) echo signals usually relies on complex hardware equipment and a large amount of scene data, which results in high costs and low efficiency. In order to simulate SAR echo signals of ship targets in the sea quickly and accurately in complex environments at a lower cost, this paper proposes a SAR echo simulation method based on model segmentation and electromagnetic scattering characteristic simulation. This method first implements the simulation of sea models under different sea conditions based on PM wave spectrum model and the Monte Carlo method, and segments them according to the requirements of simulation resolution. Then, it uses Python API 3.11 in Blender 4.5 to segment the ship model automatically and optimize the visible surface elements and mesh for each sub-model. Next, it uses Lua API in Feko to simulate the electromagnetic scattering characteristics of each sub-model of the sea and the ship target automatically, and obtains the required radar cross section (RCS) data of the ship target in the sea after processing. Finally, SAR echo simulation is realized through dual-channel technology. To further verify the simulation result, the chirp scaling (CS) algorithm is used for imaging processing. The results show that this method can realize SAR echo simulation of various ship targets under different sea conditions in a quick, accurate and cost-effective manner without the need for any hardware equipment. Full article

Waste electronic components are valuable secondary resources containing various metals. Analyzing their elemental distribution is crucial for developing recycling methods. Micro- X-ray fluorescence (μ-XRF) is commonly used for this purpose, but traditional polychromatic X-ray excitation creates high background scattering. This masks trace element signals, impairing detection limits and accurate identification of minor valuable or hazardous elements. To address this, this study developed a monochromatic μ-XRF spectrometer using a low-power molybdenum-target X-ray tube. The system integrates polycapillary lenses for X-ray regulation and a flat crystal for monochromatization, producing a micron-sized monochromatic X-ray spot with high power density. This design eliminates scattered background from the primary continuous spectrum and enhances excitation efficiency by concentrating photon flux, enabling high-brightness monochromatic beams even at low tube power. The spectrometer was validated by analyzing a waste printed circuit board. High-resolution elemental mapping successfully revealed clear distribution patterns of major elements like copper, nickel, and iron, consistent with their physical structures. These images allowed intuitive differentiation of compositional differences across functional regions. This technique effectively overcomes the background interference caused by polychromatic excitation and is expected to further enhance the quality and reliability of elemental distribution imaging. It provides a powerful tool for formulating precise, scientific recycling strategies for waste electronics. Full article

Narrowing the bandgap of wide-bandgap oxides and controlling defects are crucial ways of enhancing the properties of functional materials. One important way to develop multifunctional hybrids is to transform non-conjugated polymers into oxide nanocomposites. To expand the broad-spectrum applications of wide-bandgap oxides, ZnO-based nanocomposites were synthesised using cross-linking non-conjugated polymers via one-pot carbonisation. As polymer-derived nanocomposites exhibit significant scattering noise, the grain boundaries of the nanocomposites were filled using additives that have an electronic effect. Optimising the grain boundaries in this way significantly decreased the scattering noise, avoided large fluctuations in baseline current and enhanced the interfacial charge transfer in broadband light spectral regions. The electronic effects of the used additives can effectively passivate defects in the polymer-derived oxide nanocomposites’ aggregation state, improving photocurrent extraction. Even after storage at room temperature for two years, the optimised nanocomposite exhibited favourable photocurrent signals when excited using typical light sources at wavelengths of 650, 808, 980 and 1064 nm. This nanocomposite has potential applications in interdisciplinary fields involving light harvesting. This study provides a simple, environmentally friendly strategy to creating multifunctional hybrids using non-conjugated polymers as precursors. Full article

Background: BRAF is a core component of the RAS–MAPK signaling pathway and an established oncogenic driver in several solid tumors and selected hematologic malignancies. In myeloid neoplasms, BRAF mutations are rare, and their prevalence, molecular context, and clinical significance remain incompletely defined. Available evidence is scattered across heterogeneous reports involving acute myeloid leukemia, myelodysplastic syndromes, myeloproliferative neoplasms, and overlap myelodysplastic/myeloproliferative neoplasms, with variable descriptions of mutation subtypes, co-mutational profiles, cytogenetic associations, therapeutic approaches, and clinical outcomes. To address these gaps, this review synthesizes data from the published literature up to 2025, summarizing the distribution, genetic landscape, and clinical impact of molecularly confirmed BRAF mutations across the spectrum of myeloid neoplasms. Results: Across published cohorts, BRAF mutations occurred in less than 1% of unselected myeloid neoplasms, with enrichment in chronic myelomonocytic leukemia and therapy-related or secondary acute myeloid leukemia. Both V600E and non-V600E variants were observed, typically within a complex genomic background involving ASXL1, TET2, DNMT3A, SRSF2, and RAS-pathway mutations. Acute myeloid leukemia cases showed poor prognosis, with median overall survival measured in months, whereas myelodysplastic syndromes and chronic myelomonocytic leukemia demonstrated relatively longer survival. Targeted MAPK inhibition produced hematologic responses in selected cases but rarely resulted in durable molecular clearance. Conclusions: BRAF mutations in myeloid neoplasms are rare, heterogeneous, and usually represent secondary events in clonal evolution. Although mutation clearance appears prognostically relevant, current targeted approaches provide limited durability, underscoring the need for prospective studies in this setting. Full article

Distributed fiber optic sensing (DFOS) has emerged as a critical technology for structural health monitoring of large-scale infrastructure, offering unique advantages in terms of coverage and environmental adaptability. This review presents a comprehensive analysis of the two dominant technical routes: fully distributed sensing based on intrinsic backscattering and massive-capacity sensing based on ultra-weak fiber Bragg grating (UWFBG) networks. For backscattering-based systems—encompassing Raman, Brillouin, and Rayleigh scattering—the inherent trade-offs among signal-to-noise ratio (SNR), spatial resolution, and sensing range constitute major performance bottlenecks. This review systematically summarizes advanced demodulation and signal processing strategies designed to overcome these physical barriers, including pulse coding sequences, chaotic laser compressed correlation, and deep learning-enhanced noise reduction algorithms. In parallel, for UWFBG-based technologies, the evolution from traditional multiple-point fiber Bragg grating (FBG) array to quasi-distributed and fully distributed UWFBG network is discussed. This review highlights key breakthroughs in achieving high spatial resolution and high-speed interrogation through hybrid multiplexing, aliased spectrum reconstruction, and dispersion-based demodulation techniques. By synthesizing recent advances in modulation schemes, detection hardware, and algorithmic processing, this paper outlines the trajectory of DFOS technologies toward high-precision, long-distance, and real-time sensing networking. Full article

In remote sensing, multi-sensor data fusion enhances environmental monitoring by integrating complementary observations. A critical step in this integration is spatial resampling to a common scale. Although often regarded as a routine preprocessing operation, resampling can become a significant source of radiometric uncertainty, systematically altering scene radiance during scale transformation, especially in heterogeneous aquatic environments. In this study, we evaluate resampling-induced radiometric uncertainty and assess the physical advantages of flux-conserving resampling in multi-scale aquatic remote sensing. Using the radiometrically stable Landsat 8 OLI sensor as a reference platform, this study develops a radiometric stability–based framework to evaluate multi-scale resampling methods. Radiometric consistency in the visible bands was first evaluated using a Rayleigh scattering calibration, allowing a systematic comparison of four resampling methods across multiple spatial scales. Normalized water-leaving radiance was then retrieved using the Satellite Signal in the Solar Spectrum (6S) radiative transfer model and validated against in situ AERONET-OC measurements. Our results indicate that radiometric consistency decreases with increasing scale, while flux-conserving resampling maintains higher stability and preserves the spatiotemporal characteristics of water radiance. These findings highlight the importance of flux-conserving resampling for multi-scale radiometric fidelity and establish the proposed framework as a reference for reliable multi-source data fusion and quantitative inversion in aquatic remote sensing and beyond. Full article

Continuous-Variable Quantum Key Distribution (CVQKD), based on quantum mechanical principles, offers theoretically unconditional security and represents a crucial direction for future secure communications. However, its application in marine environments faces challenges such as high attenuation, scattering, and turbulence in seawater, severely impacting quantum signal transmission and secure key generation efficiency. Orbital angular momentum (OAM) multiplexing technology leverages the orthogonality of photon OAM modes to transmit multiple independent quantum signals in parallel within a single spatial channel. In this scheme, each OAM mode serves as an independent sub-channel, enabling simultaneous key distribution across multiple modes, thereby significantly enhancing the system’s secure key rate and spectral efficiency. This paper proposes an OAM-multiplexed CVQKD scheme tailored for marine channels. Based on Yi’s power spectrum model for marine turbulence refractive index fluctuations, we derive expressions for OAM mode probability density and detection probability. Through system modeling and performance analysis, we investigate the impact of marine turbulence on OAM modes, as well as on the secure key rate and transmission distance of CVQKD systems. Results indicate that higher-order OAM modes exhibit more pronounced turbulence effects, leading to reduced key rates and limited transmission distances. The OAM multiplexing approach significantly enhances system key rates, providing theoretical and technical references for constructing high-rate seawater quantum communication networks. Full article

This paper systematically reviews the key spectral information extraction methods in Brillouin microscopy, aiming to address the core challenge of accurately extracting material mechanical parameters from raw spectra. Based on technical principles, the methods are categorized into three types for elaboration: Spontaneous Brillouin Scattering (SpBS) is characterized by low signal-to-noise ratio (SNR) and strong background interference, and its processing relies on high-precision spectrometers and complex preprocessing procedures to mitigate noise and background effects; Stimulated Brillouin Scattering (SBS) operates on the mechanism of optical gain/loss, which achieves significantly improved data SNR and thereby enables more robust and accurate Lorentzian fitting for spectral analysis; Impulsive Stimulated Brillouin Scattering (ISBS) retrieves the frequency spectrum by inverting time-domain oscillating signals, and the core of its processing lies in super-resolution algorithms such as Fast Fourier Transform (FFT) and the Matrix Pencil Method, which are tailored to match its high-speed data acquisition capability. The paper further compares the advantages and disadvantages of various methods, outlines future development trends of intelligent processing technologies such as deep learning and multi-modal data fusion, and provides a clear guide for selecting the optimal data processing strategy in different application scenarios. Full article

A promising approach to alleviate the emerging capacity limitations in backbone optical networks is to employ the Super C+L-band, which provides an available spectrum of roughly 12 THz. Network throughput is a key metric for analyzing the performance of such networks; however, evaluating this metric is a complex task due to the interplay between physical and network layer aspects. Physical modeling, which accounts for signal impairments, is particularly complex in these scenarios due to the presence of Stimulated Raman Scattering (SRS), which transfers energy from the C to the L band. On the other hand, network layer modeling is also challenging due to the influence of numerous factors, including physical topology, routing, and traffic characteristics. For the networks considered here, we propose a machine learning approach to predict both the network throughput and the average channel capacity for the Shannon and real cases, and to investigate how these metrics depend on various physical topology parameters. The approach relies on an Artificial Neural Network (ANN) model, whose predictions are interpreted using the SHapley Additive exPlanations (SHAP) method to identify the importance of various topological parameters. Furthermore, the ANN is trained using data obtained from a previously developed simulator that takes into account both physical and network aspects. The analysis provides valuable insights for designing future ultra-high-capacity optical backbone networks. Full article

The laser heterodyne radiometer (LHR) is a promising technique for atmospheric remote sensing due to its exceptionally high spectral resolution and sensitivity. A model based on a random forest algorithm is proposed to generate highly accurate prior atmospheric profiles using real-time meteorological parameters. In addition, a locally weighted scatter plot smoothing (LOWESS) method is applied for baseline correction during data preprocessing. An inversion algorithm is implemented using the Py4CAtS radiative transfer model, in which quadratic baseline parameters are included in the iterative process. Continuous measurements of the atmospheric ${\mathrm{CO}}_2$ absorption spectrum were made in our laboratory (Hefei, China, 31.9°N, 117.16°E), and the dry mixing ratio ($X_{{\mathrm{CO}}_2}$) was obtained after data processing and inversion. The results demonstrate that this research improves the accuracy of LHR signal inversion. The implemented Python-based framework shows potential for real-time atmospheric ${\mathrm{CO}}_2$ monitoring. Full article

The Raman spectrum of adenine and the surface-enhanced Raman spectrum (SERS) upon adsorption of adenine on an Ag₈ cluster in aqueous solution were calculated using the DFT/PBE0/Def2-TZVP method with the IEF-PCM solvent model. TD-DFT calculations were performed to determine the excitation wavelengths of adenine and the Ag₈•A complex, thereby selecting excitation wavelengths compatible with available experimental Raman spectroscopy instruments. In addition, excitation wavelengths with the maximum oscillator strength were chosen to propose characteristic spectra for experimental studies. The calculated Raman activities were converted into Raman scattering intensities, and the enhancement factor EF_int was determined. The results show that an excitation wavelength of 325 nm gives the strongest and most distinct SERS signal, 532 nm provides stable signals suitable for commercial instruments, while 442 nm significantly reduces several characteristic vibrational bands. Moreover, the Ag₈ cluster exhibits excellent enhancement of the Raman signal for adenine. This study provides a basis for selecting excitation wavelengths and characteristic vibrational modes to identify adenine, supporting the development of label-free biosensors based on silver clusters. Full article