Search Results (2,302)

Two metallacyclic tetranuclear Fe(III) complexes, [{Fe₂(μ-O)(μ-RCOO)₂(tpon)}₂](BPh₄)₄ [R = Me (1), Ph (2)], where the flexible ditopic ligand tpon (N,N,N′,N′-tetrakis(2-pyridylmethyl)octane-1,8-diamine) links two μ-oxo-bis(μ-carboxylato) triple-bridged dinuclear units, have been prepared. Single-crystal X-ray diffraction establishes that both complexes adopt a 26-membered macrocyclic framework featuring an internal cavity capable of guest inclusion. Notably, incorporation of a monoatomic μ-oxo bridge enforces an outward orientation of the ligand alkyl chains, thereby suppressing the “zipper effect” observed in the previously reported Mn(II) analogue and facilitating the encapsulation of an acetone molecule. UV–vis absorption and diffuse-reflectance spectra confirm that the tetranuclear scaffold remains intact in both the solid state and in solution. These results demonstrate that modulating local coordination directionality via μ-oxo bridging is an effective strategy for controlling the global conformation and host–guest properties of large metallasupramolecular architectures. Full article

Fabry–Perot resonator (FPR) sensors are widely implemented in optical and microwave waveguides because their interference fringe spectra enable highly sensitive, stable, and calibration-free measurements. In contrast, despite the extensive use of beams and plates as waveguides in vibration- and ultrasound-based structural health monitoring (SHM), an explicit FPR framework for these mechanical waveguides has not been established. This paper demonstrates that flexural beams can be rigorously treated as FPRs despite their inherently dispersive nature. Through analytical derivation, wave-propagation analysis, and fringe-based group-velocity extraction, we show that flexural-beam resonances arise from multi-reflection interference analogous to Fabry–Perot interference. A closed-form relationship between the frequency-dependent group velocity and the FPR free spectral range (FSR) is established, enabling inverse determination of mechanical or environmental perturbance from the FPR fringe spectrum. By extending FPR-based fringe analysis to dispersive mechanical waveguides, this work introduces a theoretical framework for implementing dispersive mechanical waveguide-based FPR sensors. Full article

In the present paper, In₂O₃ NPs were synthesized by a wet-chemical method, in the absence and presence of the surfactant, and deposited as thin films on silicon substrates. After deposition, the films were subjected to rapid thermal annealing (RTA) at 550 °C, 750 °C, and 900 °C, for 300 s, under an inert atmosphere. The correlation between the morphological, structural, and optical characteristics, the wetting capacity of In₂O₃ films synthesized under different synthesis conditions, and the influence of the RTA treatment are presented. The vibrations of In-O bonds for In₂O₃ samples were confirmed using FTIR spectroscopy. Structural analysis shows that In₂O₃ NPs have a cubic crystalline structure, but with the increase in temperature at 900 °C, diffraction peaks characteristic of the tetragonal phase of indium appear, correlated with a decrease in lattice parameters, as a result of the crystallinity. The morphology of the In₂O₃ samples was studied by SEM, revealing predominantly spherical and uniformly distributed particles with nanometric sizes. The absorption spectra of the In₂O₃ NPs showed peaks in the ultraviolet region, and the high energy bandgap value of the In₂O₃ films varied between 3.28 and 4.33 eV, depending on the samples and RTA treatment. The contact angle measurements of In₂O₃ films determined the wetting capacity of the surface, reflecting changes in surface morphology and structure induced by the RTA process. The results suggest that In₂O₃ thin films with spherical nanoparticles, good wettability, and percolation can be used for the development of sensors with increased selectivity and sensitivity. Full article

Two-dimensional (2D) materials exhibit electronic and collective excitation properties that are highly sensitive to surface chemistry and thickness, requiring surface-sensitive characterization at low electron energies. Here, we investigate graphene, hexagonal boron nitride (h-BN), molybdenum disulfide (MoS₂), and titanium carbide (Ti₃C₂) MXene using an advanced home-built scanning low-energy electron microscopy system combined with time-of-flight electron spectroscopy (SLEEM/ToF). The system uniquely records electron energy-loss spectra (EELS) from transmitted electrons rather than from the reflected electrons used in conventional SLEEM. Compared with high-energy EELS, our low-energy ToF-EELS approach offers enhanced surface sensitivity and reduced beam-induced damage, enabling direct probing of π and π + σ plasmon excitations. Additionally, complementary techniques, including scanning transmission electron microscopy (STEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), were employed to characterize structural and chemical properties. EELS were acquired for all investigated 2D materials at electron landing energies of 500–1500 eV, and in the 5–50 eV range for selected materials, including graphene and MoS₂. Analysis of these spectra enabled determination of the average plasmon positions across the measured energy range for all studied materials. Furthermore, a quantitative determination of the inelastic mean free path (IMFP) was achieved for graphene in the 10–50 eV range, yielding a value of 1.9 ± 0.2 nm. These results demonstrate the potential of SLEEM–ToF for surface-sensitive analysis of 2D materials. Full article

Soil salinization is one of the primary forms of land degradation in arid and semi-arid regions, severely constraining agricultural production in Xinjiang’s oases. Unmanned aerial vehicle (UAV) imagery provides an effective means for precise monitoring of soil salinization, with image spatial resolution being a key factor affecting assessment accuracy. However, traditional single-scale remote sensing monitoring methods rely solely on spectral and textural features at the leaf scale (0.1 m resolution captures leaf-scale characteristics), neglecting the contribution of multi-scale features (single-row canopy scale and single-membrane-covered area scale (6-row crop canopy)) to soil salinity. For instance, 0.5–1 m reflects single-row canopy scale, while 2 m reflects single-membrane-covered area scale. Therefore, this study developed a multi-scale UAV imagery and machine learning framework to enhance soil electrical conductivity prediction accuracy. This study focuses on oasis cotton fields in Shaya County, Xinjiang. Based on UAV multispectral imagery, we resampled data to generate eight datasets at different spatial resolutions: 0.1, 0.5, 1, 1.5, 2, 2.5, 5, and 10 m. For each resolution, we calculated 21 spectral indices and 48 texture features to construct a feature set. At both single and multispatial scales, spectral indices, texture features, and their spectral-texture fusion features were constructed. Combining these with Backpropagation Neural Network (BPNN), Random Forest Regression (RFR), and Extreme Gradient Boosting (XGBoost) models, a soil EC estimation framework was developed. The impact of three feature combination schemes on cotton field soil conductivity estimation using single-scale UAV imagery was compared. The accuracy of soil EC estimation for cotton fields was compared between multi-spatial scale and single-scale UAV image features. The optimal combination strategy for a multi-spatial scale and multiple features was determined. Results indicate that combining spectral and texture features yields the highest estimation accuracy for cotton field soil electrical conductivity in single-scale analysis. Multi-spatial scale image features outperform single-scale image features in estimating cotton field soil electrical conductivity accuracy. By comparing different feature combinations, when integrating 0.5 m spatial-scale spectra (S1, EVI, DVI, NDVI, Int1, SI) with 0.1 m texture features (RE1_ent, R_cor, RE1_cor, G_hom, B_mea, R_con, NIR_con), the XGBoost model achieved the optimal prediction accuracy ($R^2$ = 0.693, RMSE = 0.515 dS/m), outperforming the methods using multiple features at a single scale. This study developed a novel multi-scale image feature fusion technique to construct a machine learning model. This method describes the image characteristics of soil electrical conductivity at different geographical scales, providing a reference approach for the rapid and accurate prediction of soil electrical conductivity in arid regions. Full article

We present a reproducible pipeline that converts color images into quantitative fluorescence maps by combining spectral measurement with a linear mixture model. The method is designed specifically for quantitative comparisons of Glo Germ™ on images of hands taken under different experimental conditions with controlled illumination. The emission spectrum of Glo Germ is measured using a spectral photometer and normalized to obtain its spectral power density function. This spectrum is projected into CIE XYZ coordinates and incorporated into a linear mixture model in which each pixel contains contributions from white light, UV-illuminated skin reflectance, and fluorophore emission. Component magnitudes are estimated with non-negative least squares, yielding a grayscale image whose intensity is a monotonic proxy for local fluorophore density. Spatial integration provides an image-level summary proportional to total detected material. Compared with single-channel proxies, the observer suppresses background structure, improves contrast, and remains radiometrically interpretable. Because the method depends only on measurable spectra and linear transforms, it can be reproduced across cameras and extended to other fluorophores. Full article

To address the challenges in intuitive feature discrimination and precise quantitative evaluation of cable defects, this paper proposes a diagnostic methodology utilizing the Symmetrized Dot Pattern (SDP) transform and reflection coefficient spectra. The Dung Beetle Optimizer (DBO) is introduced to adaptively optimize the SDP transform parameters, employing the Structural Similarity Index Measure (SSIM) as a fitness function to maximize discriminability between deterioration states. Three quantitative features, including the number of effective pixels, the degree of red–blue aliasing, and radial dispersion, are extracted to characterize the physical degradation processes of signal energy accumulation, angular evolution, and path divergence. By incorporating a self-reference calibration mechanism for structural differences, features are fused into a Comprehensive Deterioration Index (CDI). Experimental results on coaxial cables simulating shielding damage and thermal aging demonstrate that SDP images reveal continuous evolution patterns corresponding to defect severity. A regression model based on these patterns effectively characterizes deterioration trends. Compared to complex models, this study achieves intuitive fault identification and preliminary quantitative description of degradation trends through image feature fusion. Although the current sample size is limited, the results validate the feasibility of this method in evaluating cable deterioration severity, offering an efficient new data-processing perspective for cable condition monitoring. Full article

The performance of remote sensing-based mineral alteration extraction is significantly restricted in the vegetation area. Spectral unmixing is one of the effective methods to address the vegetation problem during mineral alteration extraction. However, the spectral curves of different tree species vary a lot; if multiple tree species are regarded as a whole during the spectral unmixing stage, the proportions of vegetation would be estimated with more errors. The purpose of this study was to verify the effects of dominant tree species classification on spectral unmixing and reconstruction, and to apply the proposed method to the mineral alteration extraction practice. To accomplish this, the Shabaosi gold deposit region in Mohe City, China, with an area of 650 km², was selected as the study area. Firstly, reference spectral curves, GaoFen-1/6 (GF-1/6) satellite imageries, ZiYuan-1F (ZY-1F) satellite imageries, Sentinel-1B satellite synthetic aperture radar (SAR) data, the ALOS digital elevation model (DEM), and sub-compartment dominant tree species data were collected; subsequently, simulated mixed-pixel reflectance images of ZY-1F, reflectance images of GF-1/6, ZY-1F, backscattering data of Sentinel-1B, slope, aspect, and 5484 tree species samples were derived from the collected data. Secondly, to verify the effect of dominant tree species classification on mineral alteration extraction, the reference spectra of pine, oak, goethite, and kaolinite were used to construct a simulated ZY-1F mixed-pixel image, and spectral unmixing and reconstruction experiments were conducted. Thirdly, fourteen independent variables were selected from the derived data, five dominant tree species classification models were trained and tested using tree species samples via the ResNet50 algorithm, and the pine- and birch-dominated parts were segmented from the ZY-1F images. Fourthly, minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer auto-clustering, and spectral angle mapper (SAM) methods were separately applied to the pine- and birch-dominated parts of ZY-1F images to extract and identify endmembers; subsequently, the fully constrained least squares (FCLS) and linear spectral unmixing (LSU) methods were separately applied to the pine- and birch-dominated parts to estimate endmember proportions and generate spectrally reconstructed ZY-1F images. Fifthly, the pine- and birch-dominated parts of spectrally reconstructed ZY-1F images were mosaiced, and the SAM was utilized to extract mineral alteration in the study area. The result showed that in the spectral unmixing and reconstruction experiment, the spectral reconstruction error declined from 0.0594 (simulated ZY-1F image without segmentation) to 0.0292 and 0.0388 (simulated ZY-1F image that was segmented by pine- and oak-dominated parts), suggesting that dominant tree species classification could improve the accuracy of spectral unmixing and reconstruction and help obtain a more reliable mineral alteration extraction result. In the study area, the tested overall accuracies (OA) and Kappa coefficients of the five dominant tree species classification models were 0.75 ± 0.03 and 0.50 ± 0.05, respectively, suggesting that conducting dominant tree species classification was feasible in dense vegetation areas and could facilitate mineral alteration extraction. After segmenting the ZY-1F image by pine- and birch-dominated parts and spectral reconstruction, eight main types of alteration, including kaolinite, vesuvianite, montmorillonite, rutile, limonite, mica, sphalerite, and quartz, were identified, and nine mineral alteration areas (MA) were delineated accordingly. Full article

Against the backdrop of global coral reef degradation, benthic resource structure is shifting from coral dominance to turf algae and detritus-dominated epilithic algal matrix (EAM). As a typical detritivorous reef fish, Ctenochaetus striatus (Quoy & Gaimard, 1825) plays an important ecological role in regulating the functioning of degraded coral reef ecosystems. Using stable isotope analysis (δ¹³C and δ¹⁵N), this study systematically compared the trophic niche characteristics of different size classes of C. striatus across four reef habitats in the Xisha Islands, South China Sea, representing a gradient of disturbance (Qilianyu Island > Lingyang Reef > North Reef > Langhua Reef), in order to elucidate habitat-specific ontogenetic shifts and their adaptive features. The results showed that C. striatus from Qilianyu Island and Lingyang Reef exhibited overall higher δ¹⁵N values, suggesting an overall pattern consistent with stronger nitrogen enrichment at the more disturbed reefs, whereas individuals from Langhua Reef had significantly lower δ¹³C values, indicating a stronger reliance on offshore-derived carbon pathways. Across size classes, the trophic niche area (SEAc) and intraspecific trophic heterogeneity, measured as mean nearest neighbor distance and standard deviation of nearest neighbor distance, of populations from Qilianyu Island, Lingyang Reef, and North Reef generally decreased with increasing body size, revealing a pattern of trophic convergence toward core resources. In contrast, the Langhua Reef population exhibited a distinct expansion–contraction pattern, suggesting flexible resource use across developmental stages under conditions of low human disturbance and high resource heterogeneity. Although smaller size classes generally showed high probabilities of niche overlap among reefs, overlap declined markedly in the largest size class, with most values falling below 50%, indicating that resource assimilation strategies increasingly reflected reef-specific resource backgrounds. These findings demonstrate that ontogenetic trophic niche shifts in C. striatus are not fixed, but are highly dependent on local resource context and habitat conditions. In degraded reefs with simplified resource structure, individuals tend to converge on core resource spectra to maintain survival, whereas in healthier reefs with greater habitat heterogeneity, they tend to show greater variation in major food sources and resource use. This study provides a theoretical basis for coral reef ecological restoration. Full article

A Principal Component-Based Spectral Standardization (PCSS) method was developed to standardize hyperspectral radiance spectra onto a fixed wavelength grid. This enables the direct comparison of radiance or reflectance spectra across different spatial pixels of an imaging spectrometer or between different instruments. The method was validated using simulated Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) spectra. The PCSS approach demonstrated high accuracy: the average root-mean-square uncertainty across all CPF channels remained below 0.07%, with maximum individual-channel uncertainties under 1%. Compared to methods based on spectral interpolation, PCSS produced significantly lower biases with tighter error distributions, particularly in spectrally rich regions. Measured Hyper Spectral Imager for Climate Science (HySICS) balloon data provided further validation. PCSS successfully estimated wavelength shifts that closely matched measured data, even when utilizing approximated Jacobians, demonstrating the method’s robustness. Because it relies on a pre-computed lookup table for model parameters, PCSS bypasses the need for intensive radiative transfer calculations, making it highly computationally efficient. Beyond CPF, this method can easily be adapted for other hyperspectral sensors by substituting their respective wavelength grids and instrument line shape functions, offering a powerful tool to improve cross-calibration between different satellite sensors. Full article

Investigation of deposystems, sediment routing, and basin architecture during Gondwana breakup refines understanding of Permian–Cretaceous landscape evolution in the central Andes. New chronostratigraphic and provenance constraints from the Eastern Cordillera and Subandean Zone of Bolivia (19–22°S) are based on U-Pb geochronology of detrital and volcanic zircons and ⁴⁰Ar/³⁹Ar dating of interbedded basalts. A discontinuous <2 km-thick Permian–Cretaceous succession records deposition in fluvial, lacustrine, alluvial fan, eolian, and shallow marine environments. Stratigraphic correlations indicate alternations between isolated half-graben subbasins and regional, non-compartmentalized basins. Detrital zircon age spectra from 18 sandstones document sediment recycling from western orogenic and magmatic arc sources and eastern cratonic basement. Synextensional successions of Early Triassic, Early Jurassic, and mid-Cretaceous age were sourced mainly from the west, including Carboniferous and Devonian rocks, while post-extensional fluvial and eolian systems were derived chiefly from the eastern craton. Variations in thickness, facies, and mafic magmatism reflect alternating extensional and neutral tectonic regimes, with localized synextensional subsidence potentially linked to extensional collapse, mantle plume activity, and South Atlantic opening. Comparison with Andean regions in Peru and Argentina indicates that episodic extension and post-extensional thermal subsidence accompanied subduction along the western margin of South America during Gondwana-Pangea breakup. Full article

Global freshwater resources face severe water quality degradation, with chlorophyll-a (Chl-a) concentration serving as a critical eutrophication indicator. While deep learning methods enable accurate Chl-a retrieval from remote sensing reflectance (Rrs) spectra, the scarcity of paired Rrs-Chl-a samples limits model generalization and causes overfitting, particularly in optically complex inland waters. To address this data bottleneck, we propose a physics-constrained latent diffusion model for synthesizing high-fidelity paired Rrs-Chl-a data to augment limited training sets for deep learning-based water quality retrieval. Our framework integrates three key innovations: (1) a lightweight variational autoencoder achieving 8.6:1 latent space compression, reducing computational overhead while preserving spectral features; (2) band-selective attention mechanisms targeting chlorophyll-sensitive wavelengths (440, 550, 680, and 700–750 nm) based on bio-optical principles; and (3) physics-guided conditional encoding that captures concentration-dependent spectral responses across oligotrophic to eutrophic regimes. Evaluated on the GLORIA dataset, our model demonstrates superior performance in spectral similarity (0.535), sample diversity (0.072), and distribution matching (Fréchet distance 0.0008) compared to conventional generative models. When applied to data augmentation, synthetic spectra improved downstream Chl-a retrieval from R²= 0.75 to 0.91, reducing RMSE by 39%. This physics-informed generative approach addresses data scarcity in aquatic remote sensing research, supporting global needs for enhanced understanding of inland and coastal water quality dynamics in data-limited regions. Full article

Near-infrared spectroscopy (NIRS) is increasingly studied as a non-invasive optical investigation tool for in vivo tissue characterization, including applications to skeletal muscle and brain regions. In this context, previous studies have demonstrated reliability in differentiating muscle sites, typically relying on dense acquisition schemes (≥50 spectra acquired per site) to ensure signal stability. However, this requirement may limit throughput and hinder real-world clinical translation. Optimizing the trade-off between acquisition burden and classification performance represents a key design problem for device scalability and feasibility of bedside deployment. In this study, we explored the impact of spectral sampling density on machine learning-based muscle discrimination. Thirty healthy adults provided 50 Vis–SWIR (Visible–Short-Wave Infrared; 350–2500 nm) reflectance spectra per biceps and triceps muscle sites (3000 spectra). Seven datasets were generated by random subsampling, progressively reducing the number of spectra (from 50 to 1 spectra/muscle/subject). All datasets underwent an identical preprocessing pipeline and were subjected to Partial Least-Squares Discriminant Analysis (PLS-DA) classification. PLS-DA achieved near-perfect discrimination from 50 to 5 spectra per muscle with a mean cross-validation (CV) accuracy ≥ 99.5%, whereas performance collapsed abruptly at three spectra (CV accuracy ~39%) and one spectrum (CV accuracy ~15%). Therefore, high machine learning classification performance is retained even when the number of acquired spectra is substantially reduced. These findings support the feasibility of acquisition-efficient protocols that may enhance device portability and reduce measurement time, thus enabling NIRS integration into clinical workflows. From a biomedical engineering standpoint, spectra number reduction without loss of predictive performance represents a key step toward scalable, real-time, and patient-centered Vis–SWIR diagnostic platforms. Full article

Canestrato di Castel del Monte (CCM) is a traditional sheep cheese from the Abruzzo region of Italy, strongly linked to local pastoral practices and characterized by high cultural and commercial value. Ensuring its authenticity is therefore essential to protect both producers and consumers. In this study, Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modelling was investigated for the classification of traditional sheep cheeses. A dataset of approximately 2000 spectra obtained from Canestrato di Castel del Monte (CCM), low-ripening CCM, and Pecorino Toscano was analyzed using different modelling strategies. Partial Least Squares Discriminant Analysis (PLS-DA) and Sequential Preprocessing through Orthogonalization combined with Linear Discriminant Analysis (SPORT-LDA) were first applied to simultaneously separate the three categories. Subsequently, a class-modelling approach based on Soft Independent Modelling of Class Analogy (SIMCA) was used to authenticate CCM and low-ripening cheeses. The discriminant models achieved excellent classification performance: accuracies close to 100% for CCM and low-ripening CCM and around 95% for Pecorino Toscano. SIMCA provided reliable rejection of non-target samples, although with lower sensitivity compared to discriminant approaches. Overall, the results demonstrate that ATR-FTIR spectroscopy coupled with appropriate chemometric modelling represents a powerful strategy for the authentication and classification of traditional sheep cheeses. Full article

This study, conducted in the Cheile Nerei–Beușnița National Park (southwestern Romania), tested the hypothesis that the life-form structure of woody plants reflects the main ecological gradients of moisture, temperature, and soil reaction in a temperate protected ecosystem characterised by sub-Mediterranean influences and a predominantly calcareous substrate. The analysis focused on the woody flora of the area, comprising 64 species belonging to 22 families, and included the assessment of life-form structure, phytogeographical spectrum, and ecological preferences based on Ellenberg indicator values. Life forms were classified according to Raunkiaer’s system, identifying megaphanerophytes, mesophanerophytes, and nanophanerophytes. The woody flora was dominated by nanophanerophytes, followed by megaphanerophytes and mesophanerophytes, indicating a complex vertical structure. The phytogeographical spectrum showed a predominance of European elements, alongside Eurasian and sub-Mediterranean components. Ecological analysis revealed a dominance of mesophilous and mesothermal species, consistent with mesic and temperate environmental conditions. Soil reaction preferences were mainly basiphilous and neutrophilous, reflecting the calcareous substrate, with vertical differentiation of ecological niches between tree and shrub layers. The high proportion of native species (>90%) and the limited presence of alien taxa indicate a high level of ecological integrity and resistance to biological invasions. Overall, the results demonstrate that the structure of woody plant life forms and their ecological preferences accurately reflect the main ecological gradients of the ecosystem. The combined use of life-form spectra and ecological indicator values provides a useful framework for assessing ecosystem structure, stability, and conservation status in temperate protected areas. Full article