Search Results (4,999)

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

The magnetic properties of molecular nanomagnets can be finely modulated by light, which provides great potential in optical switches, smart sensors, and data storage devices. Light-induced spin transition, structure changes, and radical formation could tune the static and dynamic magnetic properties of molecular nanomagnets with high spatial and temporal resolutions. Herein, we summarize the design strategies of photoresponsive molecular nanomagnets and review the recent advances in transition metal/lanthanide molecular nanomagnets with photomagnetic properties. The photoresponsive mechanism based on spin transition, photocyclization, and photogenerated radicals is discussed in detail, providing insights into the photomagnetic properties of molecular nanomagnets for advanced photoresponsive materials. Full article

As a key parameter that defines the spectral characteristics of lasers, the precise measurement of laser linewidth is crucial for a wide range of advanced applications. This review systematically summarizes recent advances in laser linewidth measurement techniques, covering methods applicable from GHz-level broad linewidths to sub-Hz ultranarrow regimes. We begin by presenting representative applications of lasers with varying linewidth requirements, followed by the physical definition of linewidth and a discussion of the fundamental principles underlying its measurement. For broader linewidth regimes, we review two established techniques: direct spectral measurement using high-resolution spectrometers and Fabry–Pérot interferometer-based analysis. In the context of narrow-linewidth lasers, particular emphasis is placed on the optical beating method. A detailed comparison is provided between two dominant approaches: power spectral density (PSD) analysis of the beat signal and phase-noise-based linewidth evaluation. For each technique, we discuss the working principles, experimental configurations, achievable resolution, and limitations, along with comparative assessments of their advantages and drawbacks. Additionally, we critically examine recent innovations in ultra-high-precision linewidth metrology. This review aims to serve as a comprehensive technical reference for the development, characterization, and application of lasers across diverse spectral regimes. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions. Full article

This work presents an image processing algorithm for the segmentation of the personalized mapping of retinal nerve fiber layer (RNFL) bundle trajectories in the human retina. To segment RNFL bundles, preprocessing steps were used for noise reduction and illumination correction. Blood vessels were removed. The image was fed to a maximum–minimum modulation algorithm to isolate retinal nerve fiber (RNF) segments. A modified Garway-Heath map categorizes RNF orientation, assuming designated sets of orientation angles for aligning RNFs direction. Bezier curves fit RNFs from the center of the optic disk (OD) to their corresponding end. Fundus images from five different databases (n = 300) were tested, with 277 healthy normal subjects and 33 classified as diabetic without any sign of diabetic retinopathy. The algorithm successfully traced fiber trajectories per fundus across all regions identified by the Garway-Heath map. The resulting trace images were compared to the Jansonius map, reaching an average efficiency of 97.44% and working well with those of low resolution. The average mean difference in orientation angles of the included images was 11.01 ± 1.25 and the average RMSE was 13.82 ± 1.55. A 24-2 visual field (VF) grid pattern was overlaid onto the fundus to relate the VF test points to the intersection of RNFL bundles and their entry angles into the OD. The mean standard deviation (95% limit) obtained 13.5° (median 14.01°), ranging from less than 1° to 28.4° for 50 out of 52 VF locations. The influence of optic parameters was explored using multiple linear regression. Average angle trajectories in the papillomacular region were significantly influenced (p < 0.00001) by the latitudinal optic disk position and disk–fovea angle. Given the basic biometric ground truth data (only fovea and OD centers) that is publicly accessible, the algorithm can be customized to individual eyes and distinguish fibers with accuracy by considering unique anatomical features. Full article

The on-orbit geometric calibration accuracy of high-resolution space cameras directly affects the application value of Earth observation data. Conventional on-orbit geometric calibration methods primarily rely on ground calibration fields, making it difficult to simultaneously achieve high precision and real-time monitoring. To address this limitation, we, in collaboration with Tsinghua University, propose a high-precision, real-time, on-orbit geometric calibration system based on active optical monitoring. The proposed system employs reference lasers to integrate the space camera and the star tracker into a unified optical system, enabling real-time monitoring and correction of the camera’s exterior orientation parameters. However, during on-orbit operation, the space camera is subjected to a complex thermal environment, which induces thermal deformation of optical elements and their supporting structures, thereby degrading the measurement accuracy of the geometric calibration system. To address this issue, this article analyzes the impact of temperature fluctuations on the focal plane, the reference laser unit, and the laser relay folding unit and proposes athermalization design optimization schemes. Through the implementation of a thermal-compensated design for the collimation optical system, the pointing stability and divergence angle control of the reference laser are effectively enhanced. To address the thermal sensitivity of the laser relay folding unit, a right-angle cone mirror scheme is proposed, and its structural materials are optimized through thermo–mechanical–optical coupling analysis. Finite element analysis is conducted to evaluate the thermal stability of the on-orbit geometric calibration system, and the impact of temperature variations on measurement accuracy is quantified using an optical error assessment method. The results show that, under temperature fluctuations of 5 °C for the focal plane and the reference laser unit, 1 °C for the laser relay folding unit, and 2 °C for the star tracker, the maximum deviation of the system’s measurement reference does not exceed 0.57″ (3σ). This enables long-term, stable, high-precision monitoring of exterior orientation parameter variations and improves image positioning accuracy. Full article

Most lacustrine dissolved organic matter (DOM) still lacks comprehensive environmental sources and molecular characterization, especially in plateau lakes. Herein, macrophytes and algae from contrasting lakes of the Yunnan-Guizhou Plateau, together with Suwannee River fulvic acid (SRFA), were used to characterize the total identified DOM (Bulk-DOM) and low-molecular-weight DOM (LMW-DOM, <200 Da). To address this, we combined spectroscopy with Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS). Algae-derived DOM (ADOM) exhibited endogenous DOM characteristics, while macrophyte-derived DOM (MDOM) showed the characteristics of endogenous and terrigenous DOM. ADOM contained numerous heteroatoms, with high proportions of proteins, carbohydrates, and lipids. The chemical structures of ADOM were more aliphatic and degradable than that of MDOM. Conversely, MDOM and SRFA had higher degree of humification and aromaticity and showed greater resistance to microbial degradation. The capability of Orbitrap MS to characterize P-containing molecules was superior to FT-ICR MS. Moreover, significant differences were found between FT-ICR and Orbitrap MS in weighted average carbon atom number, weighted average mass-to-charge ratio, carbohydrates, and P-containing compounds. LMW-DOM accounted for approximately 10% of Bulk-DOM. Compared to Bulk-DOM, LMW-DOM was more active than Bulk-DOM because of the reduced state and more N-containing compounds. This study provides a valuable perspective to reveal the molecular characteristics and behaviors of ADOM and MDOM, which has crucial implications for carbon cycling in aquatic ecosystems. Full article

The demand for high-spatial-resolution optical remote sensing applications is increasing, while conventional high-resolution optical payloads face limitations in widespread application due to their large size and high manufacturing costs. With the rapid development of image processing technology, we adopt a method combining small-pixel detector sampling with image deblurring algorithms to obtain high-spatial-resolution remote sensing images. In this work, we use Zernike polynomials to simulate diffraction-blurred small-pixel images under various aberration modulations, ensuring the simulation data follow solid physical principles. Furthermore, we propose a new weighted sparse model ℓ_we that combines the Welsch-weighted ℓ₁-norm with ℓ₀-norm constraints, and further applies ℓ_we regularization to both gradient fidelity terms and image gradient terms to enhance fidelity constraints and improve latent structure preservation. Compared with other sparse models, our model produces results with fewer residual structures and stronger sparsity. Comprehensive evaluations on both simulated small-pixel remote sensing datasets and real-world remote sensing images demonstrate that the proposed weighted sparse image quality restoration algorithm achieves more desirable results with excellent robustness. Compared to other methods, the proposed approach improves PSNR by an average of 2.5% and SSIM by 2.2%, while reducing ER by 20.7%. This provides an effective technical solution for image quality restoration of small-pixel remote sensing data. Full article

Background: Long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency (LCHADD) is a rare autosomal recessive metabolic disorder affecting long-chain fatty acid β-oxidation. A hallmark feature of LCHADD is progressive chorioretinopathy, which may lead to severe visual complications, including macular neovascularization (MNV). The goal of the study was to analyze MNV in patients with genetically confirmed LCHADD. Methods: Data of 8 patients with LCHADD from the Kaszubia region in Poland followed in the clinic were retrospectively analyzed. The analyses included genetic confirmation, ophthalmologic examinations, spectral-domain optical coherence tomography (SD-OCT), and treatment responses. Results: Two patients with MNV in the course of LCHADD were identified. In Patient 1, a 9-year-old female, unilateral MNV at the fibrotic stage with a visual acuity of counting fingers was diagnosed in the right eye. No treatment was administered. The left eye remained stable, maintaining a best corrected visual acuity (BCVA) of 0.9 on the decimal Snellen chart. Patient 2, male, was followed from age 8 to 16 and during that time developed bilateral MNV. The right eye presented with inactive MNV at the age of 9, resulting in BCVA reduction to 0.3 without active fluid, and remained stable without intervention. The left eye developed active MNV at age 15 with subretinal fluid and retinal edema. Treatment with five intravitreal injections of ranibizumab led to complete resolution and recovery of BCVA to 1.0. Conclusions: MNV, although rare, can develop in pediatric LCHADD patients silently and bilaterally. Early detection through regular ophthalmologic screening is crucial, as timely anti-VEGF treatment can preserve or restore vision. Delayed diagnosis may result in irreversible damage and limited therapeutic benefit. Full article

This study proposes a rigid-flexible neural optrode integrated with anti-bending SU-8 optical waveguides and locally soft peptide-functionalized microelectrodes to address the challenges of precise implantation and long-term biocompatibility in traditional neural interfaces. Fabricated via microelectromechanical systems (MEMS) technology, the optrode features a PBK/PPS/(PHE)₂ trilayer electrochemical modification that suppresses photoelectrochemical (PEC) noise by 63% and enhances charge storage capacity by 51 times. A polyethylene glycol (PEG)-enabled temporary rigid layer ensures precise implantation while allowing post-implantation restoration of flexibility and enabling positioning adjustment. In vitro tests demonstrate efficient light transmission through SU-8 waveguides in agar gel and a 63% reduction in PEC noise peaks. Biocompatibility analysis reveals that peptide-coated PI substrates improve cell viability by 32.5–37.1% compared to rigid silicon controls. In vivo validation in crucian carp midbrain successfully records local field potential (LFP) signals (60–80 μV), thereby confirming the optrode’s sensitivity and stability. This design provides a low-damage and high-resolution tool for neural circuit analysis. It also lays a technical foundation for future applications in monitoring neuronal activity and researching neurodegenerative diseases with high spatiotemporal resolution. Full article

The mechanism of separating the small chiral drug molecules on large soft polymers is essential in pharmaceutical science. As a case study, the differentiation mechanism of ibuprofen, (R,S)-2-(4-isobutylphenyl)propanoic acid, with cellulose tris(4-methylbenzoate) (CMB) as the chiral stationary phase (CSP) was investigated by combining the molecular docking simulation and multi-level layered terminal-to-center elongation (ML-T2C-ELG) method. Our results demonstrated that, based on the optimized geometry using the ML-T2C-ELG method, the complexation energy of S-ibuprofen with CMB obtained at B3LYP-D3(BJ)/6-311G(d) level is more negative than that of R-ibuprofen, which is caused by the greater hydrogen bonding and π-π stacking interactions between CMB and S-ibuprofen. The results are in line with the experimental observations of high-performance liquid chromatography (HPLC) that the retention time of S-ibuprofen on CMB is longer than that of R-ibuprofen. Moreover, the ML-T2C-ELG method was found to be valuable for optimizing the geometries of such flexible and large systems, which allows for a more accurate description of interactions between soft polymers and small molecules when coupled with the docking simulation. It is anticipated that this study can provide beneficial insights for future optical resolution mechanisms of other chiral drugs. Full article

Deuterium-labelled amino acids have found extensive applications in such research areas as pharmaceutical, bioanalytical, neutron diffraction, inelastic neutron scattering, in analysis of drug metabolism using mass spectrometry (MS), and, structuring of biomolecules by NMR. For these reasons, interest in new methodologies for the deuterium labelling of amino acids and the extent of their applications are equally rising. The ideal method will be able to label target compounds rapidly and cost-effectively by the direct exchange of a hydrogen atom by a deuterium atom. Most of these exchange reactions can often be carried out directly on the final target compound or a late intermediate in the synthesis, and often D₂O can be used as the deuterium source. This review aims to provide a high-level overview of the chemical deuteration of amino acids in various groups (aromatic, heterocyclic, and non-aromatic α-amino acids). It primarily focuses on metal-catalyzed H/D exchange under hydrothermal conditions, with some attention given to studies on stereoselectivity and chemically synthesized perdeuteration and selective deuteration. In addition, we present different methods tested, manipulated, and developed for versatile new scalable protocols for preparation of selective and perdeuterated biologically important amino acids and their enzymatic and kinetic resolution to give pure enantiomers. Different methods for the synthesis of stereocontrolled selective and perdeuterated amino acids, including synthetic, and methods for preparing optically pure amino acids are presented. Full article

This study focuses on a comprehensive analysis of the common methods for road infrastructure monitoring, as well as the perspective of various fiber-optic sensor (FOS) realization solutions in road monitoring applications. Fiber-optic sensors are a topical technology that ensures multiple advantages such as passive nature, immunity to electromagnetic interference, multiplexing capabilities, high sensitivity, and spatial resolution, as well as remote operation and multiple physical parameter monitoring, hence offering embedment potential within the road pavement structure for needed smart road solutions. The main key factors that affect FOS-based road monitoring scenarios and configurations are analyzed within this review. One such factor is technology used for optical sensing—fiber Bragg grating (FBG), Brillouin, Rayleigh, or Raman-based sensing. A descriptive comparison is made comparing typical sensitivity, spatial resolution, measurement distance, and applications. Technological approaches for monitoring physical parameters, such as strain, temperature, vibration, humidity, and pressure, as a means of assessing road infrastructure integrity and smart application integration, are also evaluated. Another critical aspect concerns spatial positioning, focusing on the point, quasi-distributed, and distributed methodologies. Lastly, the main topical FOS-based application areas are discussed, analyzed, and evaluated. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article