Search Results (2,107)

High-spectral-resolution retrievals of nitrogen dioxide (NO₂) provide detailed atmospheric absorption information, but they usually involve large data volume, low computational efficiency, and complex instrument requirements. To address these limitations, we employ a low-spectral-information retrieval strategy for fast atmospheric monitoring. In this study, the Discrete-Wavelength Differential Optical Absorption Spectroscopy (DWDOAS) technique is applied by selecting 14 representative wavelength samples in the 420–450 nm window. Multiple wavelength–resolution configurations are constructed and quantitatively assessed using an entropy-weighting scheme to identify the optimal setup. Using TROPOspheric Monitoring Instrument (TROPOMI) and Environmental Trace Gases Monitoring Instrument (EMI-II) measurements as case studies, we show that at a spectral resolution of ~2 nm, DWDOAS-derived NO₂ vertical column density (VCD) are highly consistent with those from conventional DOAS retrievals (correlation coefficient R > 0.7) and exhibit relative differences of approximately ±30%. Monte Carlo simulations further demonstrate method robustness, yielding mean uncertainties below 2 × 10¹⁴ molecules·cm⁻². The results indicate that DWDOAS effectively suppresses high-frequency spectral noise while preserving key differential absorption structures, thereby achieving a favorable trade-off between information retention and noise robustness. Nevertheless, increased retrieval uncertainty is observed under low-NO₂ background conditions or strong aerosol loading, which reduces sensitivity to weak absorption features. Overall, this study confirms that reliable NO₂ retrieval performance can be maintained while substantially reducing spectral information requirements, offering practical implications for low-resolution spectrometer design, onboard data compression, and rapid, wide-area atmospheric trace-gas monitoring. Full article

Image-Based Overlay (IBO) equipment leverages optical reflection imaging principles, combined with focusing and alignment strategies to measure overlay marks. Among all measurement steps, the focal plane measurement of marks exerts the most critical impact on overlay accuracy, while the time consumed by focal plane detection directly determines the overall measurement throughput. To address the trade-off between accuracy and efficiency in advanced process nodes, this paper proposes an integrated optimization strategy encompassing optical hardware design and software algorithms. The hardware solution adopts a dual-wavelength, dual-detector architecture: optimal imaging wavelengths are selected independently for the previous-layer and current-layer marks, ensuring each layer achieves ideal imaging conditions without mutual interference. The software strategy employs a deep learning framework to simultaneously predict and adjust the horizontal position (alignment) and vertical defocus number of measured marks in real time with high precision, thereby securing the optimal imaging posture. By synergizing hardware-based optimal imaging conditions and software-based posture adjustment, this method effectively mitigates the impact of background noise and system aberrations, ultimately improving both the accuracy and efficiency of overlay measurement. Full article

Precision monitoring of leaf nitrogen content (LNC) in fruit trees is critical for optimizing fertilization and fruit quality. In this study, 1120 apple-leaf samples spanning two phenological stages were collected. Characteristic wavelengths were selected using competitive adaptive reweighted sampling and the successive projection algorithm (CARS–SPA). To mitigate inefficient exploration during population initialization and iterations, we propose a collaborative enhancement strategy integrating Sobol-sequence sampling and elite opposition-based learning (EOBL), termed SEO, which simultaneously refines initialization and iterative updating in swarm-based optimization algorithms. Four machine learning algorithms were trained to construct cross-phenological-stage LNC inversion models. Results indicated characteristic wavelengths lay within the visible region. The combined SEO strategy improved search capability and efficiency, with SEO-BKA achieving the best performance. Consequently, the SEO-BKA-XGBoost model yielded the highest accuracy in the bloom and fruit-set stage (R² = 0.883; RMSE = 0.124) and fruit-enlargement stage (R² = 0.897; RMSE = 0.069). These findings provide robust technical support for LNC hyperspectral inversion in apple trees. Full article

In this study, a double-lattice photonic crystal structure was designed to achieve deep ultraviolet lasing without the use of any Distributed Bragg Reflector (DBR), which is called a photonic-crystal surface-emitting laser (PCSEL). The plane wave expansion (PWE) method was used to study the influence of various structural parameters on the resonant wavelength. Utilizing the random forest algorithm, we determined that the importance of the lattice constant to the resonant wavelength is 95.24%. Furthermore, we realized the reverse design of double-lattice photonic crystals from the target wavelength to optimal structural parameters through a radial basis function (RBF) network algorithm. Comparative analysis of the extreme learning machine (ELM) and back propagation (BP) algorithms demonstrated that RBF-based performance was notably superior to the training outcomes of other algorithms. The mean absolute error (MAE) of the lattice constant of the test set in the training results was 0.7610 nm, root mean square error (RMSE) was $1.143\times {10}^{-3}$ nm, and mean absolute relative error (MARE) was $5.489\times {10}^{-3}$. We verified the reliability of the algorithm and designed 13 groups of photonic crystals with different epitaxial structures. The mean square error (MSE) was 0.6188 ${\mathrm{nm}}^2$ compared with that of the plane wave expansion method. This work demonstrates applicability across various wavebands and epitaxial structures in GaN-based devices, providing a novel approach for the rapid iteration of deep ultraviolet PCSELs. Full article

Accurate and non-destructive assessment of plant nutritional status remains a key challenge in precision agriculture, particularly under dynamic physiological conditions such as dehydration. Therefore, this study focused on developing an integrated nutritional assessment framework for avocado (Persea americana Mill.) leaves across progressive dehydration stages using spectral analysis. A novel nutritional function index (NFI) was innovatively constructed using an entropy-weighted multi-criteria decision-making approach. This unified assessment metric integrated critical physiological indicators, such as moisture content, nitrogen content, and chlorophyll content estimated from soil and plant analyzer development (SPAD) readings. To enhance the prediction accuracy and interpretability of NFI, innovative vegetation indices (VIs) specifically tailored to NFI were systematically constructed using exhaustive wavelength-combination screening. Optimal wavelengths identified from short-wave infrared regions (1446, 1455, 1465, 1865, and 1937 nm) were employed to build physiologically meaningful VIs, which were highly sensitive to moisture and biochemical constituents. Feature wavelengths selected via the successive projections algorithm and competitive adaptive reweighted sampling further reduced spectral redundancy and improved modeling efficiency. Both feature-level and algorithm-level data fusion methods effectively combined VIs and selected feature wavelengths, significantly enhancing prediction performance. The stacking algorithm demonstrated robust performance, achieving the highest predictive accuracy (R2V = 0.986, RMSEV = 0.032) for NFI estimation. This fusion-based modeling approach outperformed conventional single-model schemes in terms of accuracy and robustness. Unlike previous studies that focused on isolated spectral predictors, this work introduces an integrative framework combining entropy-weighted feature synthesis and multiscale fusion learning. The developed strategy offers a powerful tool for real-time plant health monitoring and supports precision agricultural decision-making. Full article

The global shortage of ${}^3$He resources has created an urgent need for alternative neutron detection technologies in applications such as national security, neutron scattering, and nuclear energy. This study designed and developed a zero-dimensional planar high-efficiency thermal neutron detector based on a boron-lined multi-wire proportional chamber (MWPC) employing two distinct efficiency-enhancement approaches: a multilayer structure and grazing-incidence geometry. For ease of use, a sealed detector has been developed, eliminating the need for gas cylinders. Geant4 simulations were utilized to optimize the ${\mathrm{B}}_4\mathrm{C}$ thickness of conversion layer and evaluate $\gamma$-ray sensitivity. Prototype detectors were fabricated and experimentally validated at the 20th beamline (BL20) of China Spallation Neutron Source (CSNS). Simulation results indicate that the optimal ${\mathrm{B}}_4\mathrm{C}$ thickness varies with layer count and neutron wavelength, measuring approximately 2.0 µm at 1.8 Å and 1.5 µm at 4 Å for a 10-layer structure, with $\gamma$-ray sensitivity below $5\times {10}^{-6}$. Experimental measurements demonstrate that a five-layer detector achieved neutron detection efficiencies of 28.0 ± 1.5% at 4.78 Å and 17.8 ± 1.8% at 2.87 Å, while a two-layer detector at 11.5${}^{\circ }$ incidence attained 19.2% and 11.7%. This research lays the groundwork for developing large-area, high-efficiency, position-sensitive neutron detectors Full article

In high-precision optical systems such as laser optics, astronomical observation, and semiconductor lithography, anti-reflection coatings are crucial for light transmittance, imaging quality, and stability, but traditional designs face modeling challenges in balancing ultralow reflectivity, high wavefront quality, and manufacturability amid multi-dimensional parameter coupling and multi-objective constraints. This study addresses these by proposing a unified mathematical modeling framework integrating a Symmetric five-layer high-low refractive index alternating structure (V-H-V-H-V) with dual-scale nanostructures, employing a constrained quasi-Newton optimization algorithm (L-BFGS-B) to minimize reflectivity, wavefront root-mean-square (RMS) error, and surface roughness root-mean-square (RMS) in a six-dimensional parameter space. The Sellmeier equation is adopted to calculate wavelength-dependent material refractive indices, the model uses the transfer matrix method for the Symmetric five-layer high-low refractive index alternating structure’s reflectivity, incorporates nano-surface height function gradient correction, sub-wavelength modulation, and radial optimization, applies Zernike polynomials for low-order aberration correction, quantifies surface roughness via curvature proxies, and optimizes via a weighted objective function prioritizing low reflectivity. Numerical results show the spatial average reflectivity at 632.8 nm reduced to 0.13%, the weighted average reflectivity across five representative wavelengths in the 550–720 nm range to 0.037%, the reflectivity uniformity to 10.7%, the post-correction wavefront RMS to 11.6 milliwavelengths, and the surface height standard deviation to 7.7 nm. This framework enhances design accuracy and efficiency, suits UV nanoimprinting and electron beam evaporation, and offers significant value for high-power lasers, lithography, and space-borne radars. Full article

Light exposure profoundly influences human emotions and physiology. Yet, adolescents spend considerable time under artificial indoor lighting. Reduced daytime light exposure delays the circadian clock, negatively affecting sleep, cognition, and mood. This pilot study examined whether 470–490 nm enhanced LED lighting modulates mood, sleep quality, and attention among 65 male Korean high school students (mean age = 15.4 years) who participated in a two-week intervention. Both groups were exposed to natural daylight, but the experimental group additionally used LED lighting enriched in the 470–490 nm wavelength range, whereas the control group used LED lighting without modified spectral characteristics. Students were exposed to the assigned lighting from 08:00 to 17:00 during regular school hours for two consecutive weeks. To evaluate the effects of the two-week intervention, pre- and post-assessments included the Beck Depression Inventory (BDI-II), the Richards–Campbell Sleep Questionnaire (RCSQ), the Epworth Sleepiness Scale (ESS), the Perceived Stress Scale (PSS), and the Frankfurter Attention Inventory (FAIR), administered twice at each assessment point. The linear mixed-effect model showed a significant time × group interaction for line errors in the first FAIR trial (F (1, 52) = 5.21, p = 0.027, η² partial = 0.09), suggesting a greater relative reduction in attentional errors in the experimental group compared with the control group. No significant effects were observed for sleep- or mood-related outcomes. These results indicate the potential relevance of wavelength-optimized lighting in educational settings where sustained attention is critical. Future studies with larger samples and longer interventions are required to confirm and extend these findings. Full article

In this study, the non-destructive determination of pH, total soluble solids (TSS), total acidity (TA), reducing sugars (RS), seed total phenolic content (TPCD), and skin total phenolic content (TPCN) in Ugni Blanc grapes was performed using visible/near-infrared (Vis/NIR) spectroscopy coupled with chemometric quantitative analysis. Diffuse reflectance spectra in the 400–1507 nm range were measured using a handheld Vis–NIR spectrometer, after which the dataset was partitioned using the SPXY algorithm, accounting for joint X-Y distances. Six spectral preprocessing methods and three modeling algorithms, Partial Least Squares (PLS), Support Vector Machine Regression (SVR), and Convolutional Neural Network (CNN), were used to construct quantitative models based on full-wavelength and feature-wavelength data. Feature-based models outperformed full-spectrum models for TA, RS, and TPCN, whereas full-spectrum models performed better for pH, TSS, and TPCD. The optimal models achieved ${\mathit{R}}_{\mathit{p}}^{\mathit{2}}$ values of 0.940, 0.957, 0.913, 0.889, 0.917, and 0.871 and RPD values of 4.074, 4.798, 3.397, 2.998, 2.904, and 2.786, correspondingly. The findings highlight the applicability of Vis/NIR spectroscopy for the accurate and non-destructive prediction of key physicochemical indicators in Ugni Blanc grapes. Full article

Light-emitting diode (LED) technology allows for precise spectral tailoring in controlled-environment agriculture, with red light (R; 600–700 nm) acting as a central regulator of plant photophysiology through phytochrome (PHY)-mediated control of photosynthesis, morphology, and metabolic adjustment. This review synthesizes the current knowledge of the benefits and limitations of monochromatic and multichromatic R-containing LED systems under both optimal and saline conditions. Monochromatic R light enhances chlorophyll biosynthesis, carbon assimilation, and biomass accumulation; however, its exclusive application can restrict stomatal regulation, photoprotection, and secondary metabolism due to the absence of blue (B)- and green (G)-light-dependent signaling pathways. In contrast, multichromatic spectra incorporating R—particularly R-B, R-far-red (R-FR), and R-centered multi-spectral combinations with white (W) or G wavelengths—provide broader physiological advantages. These include improved photosystem II efficiency, pigment stability, ion homeostasis, antioxidant defense, and metabolic quality, while also optimizing canopy light distribution and energy use efficiency. Under salinity stress, R-containing spectral combinations consistently outperform monochromatic R by enhancing osmotic adjustment, reducing oxidative damage, and maintaining photosynthetic integrity. Nevertheless, species-specific sensitivity, ratio-dependent responses, and potential risks such as excessive elongation under FR enrichment highlight the need for careful spectral optimization. Despite substantial progress, the mechanisms underlying the integration of PHY signaling with salinity-responsive networks remain insufficiently resolved. Advances in multi-omics approaches and dynamic spectral management will be critical for the development of R-based LED strategies that sustainably enhance crop performance and stress resilience in controlled environments. Full article

The mapping of vineyard cultivars presents a substantial challenge in digital agriculture due to the crop’s high intra-class heterogeneity and low inter-class variability. High-dimensional spectral datasets, such as hyperspectral or spectrometry data, can overcome these difficulties. However, research has yet to fully address the need for optimal spectral feature subsets tailored for grapevine cultivar discrimination, while few studies have systematically examined waveband subsets that transfer effectively across different learning algorithms. This study sets out to address these gaps by introducing a Partial Least Squares (PLS)-based ensemble feature selection framework with Weighted Borda Count aggregation for cultivar discrimination. Using in-field spectrometry data, collected for six cultivars, and 18 PLS-based feature selection methods spanning filter, wrapper, and hybrid approaches, the PLS–ensemble identified 100 wavebands most relevant for cultivar discrimination, reducing dimensionality by ~95%. The efficacy and transferability of this subset were evaluated using five classification algorithms: Oblique Random Forest (oRF), Multinomial Logistic Regression (Multinom), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (CNN). For oRF, Multinom, SVM, and MLP, the PLS–ensemble subset improved accuracy by 0.3–12% compared with using all wavebands. The subset was not optimal for the 1D-CNN, where accuracy decreased by up to 5.7%. Additionally, this study investigated waveband binning to transform narrow hyperspectral bands into broadband spectral features. Using feature multicollinearity and wavelength position, the 100 selected wavebands were condensed into 10 broadband features, which improved accuracy over both the full dataset and the original subset, delivering gains of 4.5–19.1%. The SVM model with this 10-feature subset outperformed all other models (F1: 1.00; BACC: 0.98; MCC: 0.78; AUC: 0.95). Full article

A method utilizing quaternion principal component analysis (QPCA) for three-dimensional fluorescence spectral (3D FS) feature extraction is employed to identify frying oil in edible oil. Particle swarm optimization partial least squares support vector machine (PSO-LSSVR) is utilized for detecting frying oil concentration. The study includes rapeseed oil, soybean oil, peanut oil, blending oil, and corn oil samples. Adulteration involves adding frying oil to these edible oils at concentrations of 0%, 5%, 10%, 30%, 50%, 70%, and 100%. Firstly, the F7000 fluorescence spectrometer is employed to measure the 3D FS of the adulterated edible oil samples, resulting in the generation of contour maps and 3D FS projections. The excitation wavelengths utilized in these measurements are 360 nm, 380 nm, and 400 nm, while the emission wavelengths span from 220 nm to 900 nm. Secondly, leveraging the automatic peak-finding function of the spectrometer, a quaternion parallel representation model of the 3D FS data for frying oil in edible oil is established using the emission spectra data corresponding to the aforementioned excitation wavelengths. Subsequently, in conjunction with the K-nearest neighbor classification (KNN), three feature extraction methods—summation, modulus, and multiplication quaternion feature extraction—are compared to identify the optimal approach. Thirdly, the extracted features are input into KNN, particle swarm optimization support vector machine (PSO-SVM), and genetic algorithm support vector machine (GA-SVM) classifiers to ascertain the most effective discriminant model for adulterated edible oil. Ultimately, a quantitative model for adulterated edible oil is developed based on partial least squares regression, PSO-SVR and PSO-LSSVR. The results indicate that the classification accuracy of QPCA features combined with PSO-SVM achieved 100%. Furthermore, the PSO-LSSVR quantitative model exhibited the best performance. Full article

We propose a strain-optimized design strategy for lattice-matched InAs_1−xSb_x/Al_1−yIn_ySb Type-I quantum wells (QWs) that emit across the near-to mid-infrared spectrum (2–5 µm). By combining elastic strain energy minimization with band offset calculations, we identify Type-I alignment for Sb contents (x ≤ 0.40) and In contents (0.10 < y ≤ 1). At the same time, Type-II dominates at higher Sb compositions (x ≥ 0.50). Using the transfer matrix method under the effective mass approximation, we demonstrate precise emission tuning via QW thickness (L_W) and compositional control, achieving a wavelength coverage of 2–5 µm with <5% strain-induced energy deviation. Our results provide a roadmap for high-efficiency infrared optoelectronic devices, addressing applications in sensing and communications technologies. Full article

In this study, the optical gain characteristics of a green laser sample based on a III-Nitride InGaN single-quantum-well structure were investigated. The Green gap phenomenon, caused by bandgap fluctuations due to inhomogeneous indium composition and the quantum-confined Stark effect (QCSE), has been a major obstacle in achieving high efficiency and high output in green-light-emitting devices. To address these issues, a sample grown on a (0001)-oriented GaN substrate with a single-quantum-well active layer was fabricated to suppress In composition non-uniformity and enhance the overlap of electron and hole wavefunctions. The optical gain behavior was analyzed using the Variable Stripe Length Method (VSLM) under various excitation densities and stripe lengths ($L_{cav}$). The results showed that as the stripe length increased, the spectral linewidth decreased and stimulated emission occurred at lower excitation densities. However, excessive cavity length led to gain saturation and a red shift in the peak wavelength due to Joule heating effects. These findings provide essential insights for determining the optimal cavity length in laser diode fabrication and are expected to serve as fundamental guidelines for improving the efficiency and output power of III-Nitride-based green laser diodes. Full article

Surface plasmon resonance (SPR) sensors based on photonic crystal fibers (PCFs) hold significant promise for high-precision detection in biochemical and chemical sensing. However, achieving high sensitivity in low-refractive-index (RI) aqueous environments remains a formidable challenge due to weak light-matter interactions. To address this limitation, this paper designs and proposes a novel dual-channel D-shaped PCF-SPR sensor tailored for the refractive index range of 1.34–1.40. The sensor incorporates a dual-layer gold/titanium dioxide film, with gold nanoparticles deposited on the surface to synergistically enhance both propagating and localized surface plasmon resonance effects. Furthermore, a D-shaped polished structure integrated with double-sided microfluidic channels is employed to significantly strengthen the interaction between the guided-mode electric field and the analyte. Finite element method simulations demonstrate that the proposed sensor achieves an average wavelength sensitivity of 5733 nm/RIU and a peak sensitivity of 15,500 nm/RIU at a refractive index of 1.40. Notably, the introduction of gold nanoparticles contributes to an approximately 1.47-fold sensitivity enhancement over conventional structures. This work validates the efficacy of hybrid plasmonic nanostructures and optimized waveguide design in advancing RI sensing performance. Full article