Search Results (893)

Rapid and reliable flood segmentation utilizing optical remote-sensing imagery is critical for effective flood disaster response and risk assessment. Nevertheless, current models frequently struggle with imprecise boundary delineation and fragmented predictions in complex environments, especially where floodwater displays high spectral variability and closely resembles shadows, dark pavements, or wet soil. To overcome these challenges, we introduce FloodSeg, an innovative Mamba-CNN encoder–decoder network incorporating two lightweight yet highly effective components: a Shift module and a sequence-shuffle module. The spatial Shift module leverages spatially shifted feature aggregation to fortify boundary-aware representations, thereby ensuring the continuity of inundation contours even under varying illumination and cluttered backgrounds. Meanwhile, the sequence-shuffle module reorganizes multi-scale features via sequence-wise mixing and cross-regional interaction, significantly enhancing long-range dependency modeling. This facilitates the generation of globally consistent flood masks while mitigating local overfitting to dataset-specific textures. Evaluated on the Kaggle and FloodNet benchmark datasets, FloodSeg achieves outstanding mIoU scores of 81.85% and 91.21%, respectively. By outperforming various state-of-the-art CNN-, Transformer-, and Mamba-based baselines, our model demonstrates a superior accuracy-efficiency trade-off. These results substantiate that FloodSeg significantly advances boundary recognition and overall segmentation completeness, establishing it as a robust and practical solution for real-world remote-sensing flood mapping applications. Full article

The development of new technologies enabling rapid, frequent, and reagent-free monitoring of kidney function is recognized as being of paramount importance. In this work, mid-(MIR) and near-infrared (NIR) spectroscopy were compared for the prediction of key renal biomarkers—creatinine, urea and albumin—using 54 serum solutions mimicking the biochemical profiles of five stages of chronic kidney disease (CKD). MIR spectra were acquired in a high-throughput microplate platform after a simple dehydration step, while the NIR spectra were obtained directly from liquid serum using a fiber optic probe. After evaluating several spectral pre-processing methods and targeted spectral regions, excellent regression models (R² > 0.9 for the best models) were obtained for the three biomarkers. MIR provided highly accurate urea predictions, whereas optimized NIR sub-regions enabled excellent estimation of creatinine and albumin. Both MIR and NIR, associated with supervised classification methods, enabled us to successfully distinguish healthy from diseased profiles and to identify the diseases state with AUC > 0.93. These findings highlight the complementary value of MIR and NIR spectroscopy for kidney disease assessment and their potential integration into point-of-care diagnostic systems. Full article

This work presents an enhanced photomechanical optical sensor inspired by our previously reported bio-inspired uncooled infrared detector. Performance improvement is achieved by strengthening the interfacial bond between the photothermal dendrite—polydopamine nanoparticle (PDA NP)/polydimethylsiloxane (PDMS) composite—and the piezoresistive laser-induced nanocarbon film, with a flexible PDMS substrate that provides both thermal insulation and mechanical stability. The resulting sensor exhibits a responsivity of 51.6 W⁻¹ under 808 nm irradiation, an order-of-magnitude enhancement over the unmodified device. Wavelength-dependent characterization (455–1550 nm) shows responsivity decreasing from 93.1 W⁻¹ at 455 nm to 14.4 W⁻¹ at 1550 nm, with response times on the order of seconds across this range. Extending this trend into the longer-wavelength region of blackbody radiation, the mechanism transitions to a predominantly bolometric mode. The device also demonstrates stable detection of several hundred microwatts and robust durability at 455 nm. These results validate interface engineering strategy as a viable pathway toward high-performance uncooled optical detection, advancing bio-inspired detectors from functional mimicry toward an application-ready platform. These findings confirm PDA NPs as effective photothermal converters primarily at shorter wavelengths, while the wavelength-dependent response suggests future tailoring of spectral sensitivity using long-wavelength-absorbing materials. Full article

In cold arid regions, the relationships between dissolved organic matter (DOM) characteristics and heavy metal distributions across ice, water, and sediment interfaces remain insufficiently resolved. This study characterized DOM spectral features and examined their associations with measured metal distributions in a typical frozen eutrophic lake using excitation–emission matrices coupled with parallel factor analysis (EEMs-PARAFAC), ultraviolet-visible absorption spectroscopy (UV-Vis), and Fourier-transform infrared spectroscopy (FTIR). Protein-like substances dominated ice DOM, whereas water and sediment-derived DOM contained more humified fluorescent components. Fluorescence indices confirmed a primarily biological origin across all media, with ice showing the highest autochthonous microbial contribution (BIX = 1.23) but the lowest humification (HIX = 0.26), suggesting a greater contribution of recently produced protein-like fluorescent DOM in the ice samples. Water DOM showed the highest average HIX (1.88), followed by sediment-derived DOM (0.61) and ice DOM (0.26). The measured hydrochemical conditions, including weak alkalinity, elevated total dissolved solids (TDS), and locally low dissolved oxygen, provide environmental context for differences in metal distributions. Exploratory Spearman analysis at 17 matched water stations identified the strongest DOM–metal associations for HIX-As (rho = 0.474, p = 0.054) and FI-Zn (rho = 0.471, p = 0.056), indicating that DOM optical properties provide testable indicators of metal-distribution patterns but should be combined with direct binding and speciation measurements for mechanistic confirmation. Because ice was collected in January 2021, whereas water and sediment were collected in October 2020, cross-medium differences are interpreted as between-campaign associations rather than synchronous partitioning. These findings provide a basis for targeted winter monitoring and future binding, speciation, and freeze-concentration experiments in shallow eutrophic lakes. Full article

Breath aerosol analysis requires low-cost sensing substrates capable of capturing aerosolized biomolecular components while preserving chemically interpretable readouts. Here, methylcellulose–phenol red (MCPR) films are evaluated as multimodal sensing substrates using model bioaerosols consisting of sodium sulfate, bovine serum albumin (BSA), and polystyrene latex particles under acidic, neutral, and alkaline pH conditions. ATR-FTIR spectroscopy revealed inverse pH-dependent trends in sulfate (1000–1100 cm⁻¹) and protein amide (1500–1700 cm⁻¹) spectral regions. A sulfate-to-protein AUC ratio increased from 0.86 ± 0.01 at pH 4 to 3.56 ± 0.32 at pH 10, demonstrating ratiometric compositional discrimination of ionic and proteinaceous aerosol fractions. UV–Vis spectroscopy showed pH-dependent λmax shifts from 432 to 556 nm, confirming the preservation of phenol red optical responsiveness after aerosol exposure. FTIR-derived ratio metrics correlated linearly with optical responses, indicating coupled vibrational and optical sensing behavior. SEM-EDS analysis of methylcellulose capture films confirmed deposition of sulfate, proteinaceous, and particulate aerosol components, supporting the platform’s suitability for multimodal spectroscopic sensing. These findings establish MCPR films as integrated capture-and-sensing substrates capable of coupling optical pH responsiveness with label-free vibrational analysis, supporting future development of low-cost breath-relevant aerosol sensing platforms. Full article

In complex mountainous environments, the asynchronous development between external color turning and internal sugar accumulation (often termed “false maturity”) in coffee cherries poses a severe challenge to post-harvest quality sorting and the consistency of final coffee products. To overcome the limitations of single-phenotype detection in raw material screening, this study proposed a multimodal quality discrimination framework integrating fruit hyperspectral imaging, micro-topography, and plant physiological characteristics. Taking typical mountain-grown fresh coffee cherries as the research object, and after comparing various spectral preprocessing and feature dimensionality reduction algorithms, the multimodal fusion efficacy of nine machine learning classifiers was systematically evaluated. The results demonstrated that: (1) Full-spectrum difference analysis quantitatively confirmed the limitations of visual harvesting; spectral reflectance differences between high- and low-sugar fruits were highly concentrated in the red and red-edge regions, with the maximum difference precisely located at 676 nm. (2) Compared to the single-spectrum model (mean accuracy of 75.93%), the fully fused Multilayer Perceptron (MLP) network effectively mitigated background noise induced by heterogeneous environments, improving the mean classification accuracy to 77.22% with a mean Area Under the Curve (AUC) of 0.827. (3) Correlation analysis clarified the quantitative association between topography and quality; micro-topographic slope (r = 0.346) was identified as the key environmental driver of spatial differentiation in fruit sugar content, while plant chlorophyll A content (r = 0.183) exhibited a corresponding physiological response trend. This study not only explains the root cause of visual assessment failure from a physical optics perspective but also reveals the spatial variation laws of quality driven by micro-topography, providing preliminary data support for the intelligent sorting of raw materials and ensuring post-harvest quality consistency of mountainous crops. Full article

Accurate lake boundary extraction from optical remote sensing imagery remains challenging in high-altitude regions such as the Tibetan Plateau due to ice cover, snow, shadows, and spectrally similar backgrounds. Although recent deep learning models achieve strong region-overlap performance, they often fail to ensure stable shoreline localization. To address this issue, we propose a Global–Local Residual Refinement Network (GLR-Net) for boundary-aware lake extraction from remote sensing imagery. The proposed framework first captures large-scale semantic context through a global branch and subsequently performs patch-level residual refinement to improve local shoreline geometry. A global-to-local guidance mechanism is further introduced to incorporate structural priors into local refinement. Experiments on a manually annotated Tibetan Plateau lake dataset demonstrate that the proposed method achieves competitive region-level segmentation performance while improving geometric shoreline accuracy. Compared with representative semantic segmentation baselines, including U-Net, SegFormer-B0, SegFormer-B4, and OCRNet, the proposed method achieves the highest Boundary F1 score of 0.811 under a 3-pixel tolerance and the lowest mean BDE of 13.19 pixels. The results indicate that conventional overlap-based metrics alone are insufficient for evaluating shoreline delineation quality in complex alpine environments. Full article

Monitoring biodiversity in coastal and marine ecosystems is essential for supporting conservation strategies, sustaining ecosystem services, and meeting policy commitments at multiple scales, including the European Union’s Habitats Directive, Sustainable Development Goal 14 (SDG 14, Life Below Water), and the Kunming–Montreal Global Biodiversity Framework (GBF). However, many benthic habitats remain insufficiently mapped or monitored due to the spatial, temporal, and logistical limitations of traditional field-based approaches. Optical Remote Sensing (ORS), based on the use of optical sensors to retrieve spectral information from shallow-water environments, has emerged as a powerful tool for mapping and monitoring these ecosystems. This study presents a systematic review aimed at providing a comprehensive synthesis of above-water ORS applications for benthic biodiversity and habitat monitoring over the period 2014–2023. A total of 179 peer-reviewed studies were analyzed to identify temporal trends, geographic patterns, target ecosystems, and methodological workflows. The review considered observation platforms including satellite, airborne, unmanned aerial vehicles (UAVs), and field spectrometry systems, together with key preprocessing procedures required for reliable benthic detection, such as atmospheric correction, water column correction, and sunglint removal, alongside validation using independent measurements. The analysis reveals a rapid expansion of ORS applications, with a strong geographic concentration in tropical and subtropical regions. Studies focusing on specific benthic groups predominantly target coral reefs and seagrass ecosystems, although many adopt integrative benthic habitat classifications that incorporate multiple benthic components at the habitat level. However, significant limitations persist, including inconsistent preprocessing workflows, limited reporting transparency, and the underrepresentation of several ecologically important taxa (e.g., annelids, mollusks, echinoderms). Despite these challenges, ORS has become a cornerstone of large-scale and repeatable coastal monitoring. By analyzing methodological practices, ecological targets, and geographic biases, this review provides a critical foundation for improving the robustness, scalability, and global applicability of ORS in benthic habitat mapping, biodiversity monitoring, and ecosystem-based management. Full article

This study presents a comprehensive first-principles investigation of the structural, elastic, electronic, and optical behavior of cubic CsCaF₃ under hydrostatic pressure. The material is confirmed to be a stable Pm-3m fluoride perovskite, with a lattice constant of $4.496 \AA$ and a tolerance factor of $0.902$. At ambient conditions, CsCaF₃ exhibits high intrinsic stiffness ($C_{11}=107.88 \mathrm{G}\mathrm{P}\mathrm{a}$, $B=53.07 \mathrm{G}\mathrm{P}\mathrm{a}$, $G=29.16 \mathrm{G}\mathrm{P}\mathrm{a}$, $E=73.94 \mathrm{G}\mathrm{P}\mathrm{a}$) and maintains mechanical stability while becoming progressively stiffer under compression. The electronic structure reveals a wide indirect band gap of $7.1 \mathrm{e}\mathrm{V}$ that broadens to $8.43 \mathrm{e}\mathrm{V}$ and transforms into a direct gap at elevated pressures. Optical calculations show strong transparency in the visible range, with a low refractive index ($1.58$) and reflectivity ($~5\%$), and a deep-UV absorption edge near $6 \mathrm{e}\mathrm{V}$. Pressure enhances these features, increasing the refractive index to 1.66 and the maximum reflectivity to 45.87% at $24 \mathrm{G}\mathrm{P}\mathrm{a}$. The plasmon resonance also displays pronounced tunability, blue-shifting from $29.56$ to $30.79 \mathrm{e}\mathrm{V}$ with a fourfold rise in intensity. Analysis of the effective-electron number further indicates pressure-driven redistribution of spectral weight within the UV region. Together, these findings demonstrate that CsCaF₃ combines robust structural stability with highly pressure-tunable optical and plasmonic responses, positioning it as a promising candidate for deep-UV optoelectronics, photonic coatings, and pressure-responsive optical technologies. Full article

This study evaluates the effect of decorative ceramic screen printing on the optical and photovoltaic performance of glass covers intended for building-integrated photovoltaics (BIPV). Nine ceramic-printed glass samples with different colors and optical densities were compared with a 4 mm Optiwhite reference glass and a bare silicon solar cell. The samples were characterized by UV-VIS-NIR spectrophotometry, energy-dispersive X-ray spectroscopy (EDS), and electrical measurements under simulated AM 1.5G irradiation at 1000 W/m². The optical results showed that the Optiwhite reference provided the highest transmittance, whereas the printed samples exhibited lower transmission, typically in the range of 60–80% in the visible region, depending on the coating type. Among the decorative variants, sample 1 showed the highest transparency, while sample 6 exhibited the lowest transmittance. The spectral behavior of the coated glasses indicates that the ceramic layers modify the photon flux reaching the solar cell through wavelength-dependent absorption and scattering effects. The photovoltaic measurements confirmed a clear relationship between decorative coating and electrical performance. Relative to the Optiwhite-covered reference cell, the printed samples showed power losses ranging from approximately 17% to 32%, with sample 1 achieving the highest maximum power among the decorative variants at 1.41 W, and sample 4 the lowest at 1.16 W. The main electrical effect of the ceramic coatings was a reduction in short-circuit current, whereas the open-circuit voltage remained nearly constant across the tested samples. EDS analysis identified the presence of ceramic-layer constituents associated with silica-, zinc-, titanium-, iron-, cobalt-, aluminum-, and fluorine-containing compounds, supporting the interpretation of vitrified decorative coatings formed during high-temperature processing. Overall, the results demonstrate that decorative ceramic printing can provide a practical compromise between architectural appearance and photovoltaic output when the optical density of the coating is appropriately controlled. Full article

A structurally simple three-layer optical absorber is proposed and systematically investigated, consisting of a continuous Ti ground plane, a SiO₂ dielectric spacer, and a Ti tetrahedral nanostructure. The absorber is constructed on a periodic square unit cell, where the lateral dimension directly determines the base width and sidewall inclination angle of the tetrahedral structure, thereby enabling effective modulation of the optical response. Full-wave electromagnetic simulations performed using COMSOL Multiphysics (version 6.0) are employed to evaluate the influence of geometric parameters on broadband absorption behavior. The optimized structure achieves a near-unity absorptivity of 0.9999 at 200 nm and maintains an effective absorption bandwidth (absorptivity > 0.9) spanning 200–3000 nm, covering the ultraviolet, visible, and near-infrared spectral regions. Parametric analysis reveals that the tetrahedral height primarily governs long-wavelength extension through enhanced optical path length, graded-index transition, and improved electromagnetic field confinement, while the unit cell width strongly influences impedance matching and localized field localization. In contrast, the Ti ground layer thickness exhibits minimal influence once it exceeds the optical skin depth, confirming its primary role as a transmission-blocking reflective substrate. Impedance retrieval analysis shows that the real part of the normalized impedance remains close to unity and the imaginary part approaches zero over most of the operating range, demonstrating that the ultrabroadband absorption behavior is dominated by effective impedance matching rather than isolated narrowband resonances. Furthermore, electric and magnetic field distribution analyses reveal that electromagnetic energy dissipation is concentrated near the tetrahedral apex and metal–dielectric interfaces, indicating the coexistence of localized plasmonic modes, cavity-assisted absorption, and multi-scale optical confinement. Full article

Radio-over-Fiber (RoF) integrated with Free-Space Optical (FSO) communication as a fronthaul is a promising solution for next-generation wireless systems, but severely suffers from the frequency-selective characteristics of hybrid RoF-FSO channels. This paper presents a measurement-driven, deployment-oriented optimization that jointly performs structure-aware pilot placement and sixth-order polynomial regression channel interpolation to enhance spectral efficiency and signal quality in quasi-static indoor FSO environments. Differential channel analysis across three transmission scenarios—Electrical Back-to-Back (B2B), Fiber B2B, and FSO—identifies critical subcarriers with high frequency-selective variation that require dense pilot allocation. A gradient-based algorithm positions 50 pilots with dense spacing (every 3 subcarriers) in critical regions and sparse spacing (every 9 subcarriers) in stable regions, reducing pilot overhead by 26.5% and increasing data capacity by 5.3% (340 → 358 subcarriers) compared to uniform placement of 68 pilots. Sixth-order polynomial regression models the non-linear channel frequency response, overcoming limitations of conventional linear interpolation. Experimental validation on a 4-QAM RoF-OFDM system over 40.6 MHz bandwidth shows that structure-aware pilot placement alone reduces Error Vector Magnitude (EVM) by 15.9%, while polynomial regression alone improves it by 15.7%. Combined optimization of structure-aware pilot placement with polynomial regression interpolation achieves 23.5% EVM reduction and 460× lower BER, equivalent to 3.2 dB SNR gain at BER = ${10}^{-6}$. Comparative analysis of four system configurations confirms consistent performance advantages across SNRs of 12–30 dB. The proposed measure-once, optimize-forever paradigm requires only one-time channel characterization, making it suitable for short-range controlled quasi-static indoor FSO links in 5G/6G fronthaul, optical wireless networks, and inter-building backhaul applications. Full article

Seismically triggered landslides represent a major secondary hazard of earthquakes, often causing widespread damage over large areas. Rapid and reliable mapping of such phenomena is therefore essential, particularly in emergency contexts. While numerous studies have addressed landslide detection in vegetated regions using optical remote sensing, arid and desert environments remain relatively underexplored due to the limited spectral contrast between stable and failed slopes. In this study, we evaluate the potential of an automatic pixel-based method for the rapid detection of seismic landslides in arid settings, using high-resolution optical imagery. The analysis focuses on the Mw 5.5 earthquake that struck the Northern Red Sea Region of Eritrea on 26 December 2022. A detailed inventory of 1393 coseismic landslides was manually mapped from pre- and post-event PlanetScope multispectral images and used both for geomorphological and macroseismic analyses and as training data for a threshold-based classification approach. Landslide detection was based on changes in the Redness Soil Index (RSI) and its differential (ΔRSI), combined with a One-Class Asymmetric Robust Gaussian classifier. Results show a good capability to delineate landslide-affected areas, although commission errors remain significant. Despite these limitations, the proposed approach, still in need of a more trained implementation in the future, proves its potential effectiveness for rapid mapping purposes, owing to its simplicity and minimal computational requirements. These results open the possibility to implement a fully automatic methodology in the future, when more landslides will be mapped and a model trained on different and normalized datasets will be implemented. The results demonstrate that pixel-based optical methods, particularly those relying on red-band spectral changes, represent a valuable tool for the preliminary assessment of earthquake-induced landslides in arid environments and may support emergency response and first-order hazard evaluation. Full article

The photoreceptor bacteriorhodopsin (HsBR) from Halobacterium salinarum is a model system for studying ultrafast photoinduced reactions in proteins. Recent time-resolved serial femtosecond crystallography (TR-SFX) experiments require high pump energies, raising concerns about nonlinear excitation and multi-photon effects. Here, we systematically investigate the influence of excitation energy, pulse duration and the sign of the chirp on the initial HsBR photo-reaction using femtosecond Vis-pump IR-probe spectroscopy in the retinal C=C stretching region. An acousto-optic programmable dispersive filter enabled independent control of pulse energy and chirp. Within the tested range, the retinal dynamics were independent of pulse duration and chirp, indicating that fluence alone does not fully describe excitation conditions. Increasing excitation energy leads to nonlinear saturation of the retinal signals and the appearance of an additional band near 1550 ${\mathrm{cm}}^{-1}$. However, this band rises linearly with the excitation energy. Hence, the additional band is not directly caused by non-resonant multi-photon absorption. Spectral decomposition reveals two components: a low-energy contribution consistent with the known retinal isomerization dynamics and a high-energy contribution attributed to a small population of photo-damaged HsBR likely formed via a resonant two-photon process. These findings clarify the role of excitation conditions in ultrafast HsBR spectroscopy and suggest that spectral changes at high pump energies mainly arise from damaged species upon resonant two-photon excitation. Full article

This work presents a Fourier-domain encryption scheme for multiplexed image databases that integrates virtual-optical multiplexing with chaotic diffusion. By combining chaotic encryption with spectral-domain symmetry reduction, the proposed approach secures large multiplexed image datasets while reducing memory requirements and preserving reconstruction fidelity. A dataset of 2025 grayscale images ($512\times 512$ pixels) is multiplexed and encrypted using linear chaotic transformations applied separately to the amplitude (A) and phase ($\varphi$) components. To improve storage efficiency, the symmetry conditions of both spectral components are exploited, allowing a reduced portion of the Fourier plane to be stored while preserving accurate reconstruction. A performance landscape relating the correlation coefficient (CC), memory consumption, and the retained Fourier-plane percentage (FPP) is constructed to identify stable operating regions that balance reconstruction fidelity and compression under increasing multiplexing load. The encryption key consists of a 22-symbol ASCII string from which 84 seed parameters for a deterministic pseudorandom chaotic map are derived. Security and sensitivity analyses demonstrate strong key dependence and resistance to statistical attacks, while maintaining high reconstruction fidelity. The proposed scheme provides an efficient and scalable solution for secure large-scale image repositories. Full article