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15 pages, 1009 KB  
Article
Optimization of Convex Transmissive Volume Bragg Grating for Hyperspectral Imaging Applications
by Yueying Li, Jiazhu Duan, Xiangjie Zhao, Yingnan Peng, Yongquan Luo, Dayong Zhang and Yibo Chen
Optics 2025, 6(4), 49; https://doi.org/10.3390/opt6040049 - 7 Oct 2025
Abstract
The Volume Bragg Grating (VBG) imaging technique provides a novel approach to gaze-type hyperspectral imaging. However, collimation constraints of the incident beam during narrow-band filtering and high-spatial-resolution imaging introduce system complexity, hindering miniaturization and modularization of the optical system. To address these limitations, [...] Read more.
The Volume Bragg Grating (VBG) imaging technique provides a novel approach to gaze-type hyperspectral imaging. However, collimation constraints of the incident beam during narrow-band filtering and high-spatial-resolution imaging introduce system complexity, hindering miniaturization and modularization of the optical system. To address these limitations, this paper proposes a convex transmissive VBG structure with tunable design parameters to enhance the field of view (FOV), relax collimation requirements, improve imaging quality, narrow filter spectral bandwidth, and simplify the optical system design. For the precise analysis and optimization of convex VBG performance, we established a physical model for filtered imaging using a convex transmissive VBG with polychromatic extended sources. An evaluation metric termed the “Maximal Splitting Angle (MSA)” was introduced to quantify the dispersion extent of image spots. This approach was employed to investigate the intrinsic correlations between structural parameters (such as the radius of curvature, vector tilt angle, grating period, and thickness) and key system performance indicators (spatial resolution and spectral resolution). The necessity of optimizing these parameters was rigorously demonstrated. Theoretical analysis confirms that convex transmissive VBG achieves superior spatial and spectral resolution over planar VBG under reduced collimation constraints. The experimental results show a 58.5% enhancement in spatial resolution and a 63.6% improvement in spectral bandwidth for the convex transmissive VBG system. Crucially, while planar transmissive VBG suffers from stray fringe interference during wavelength tuning, its convex counterpart remains unaffected. This study proposes a novel device structure, offering new perspectives for optimizing VBG-filtered spectral imaging systems. Full article
(This article belongs to the Topic Color Image Processing: Models and Methods (CIP: MM))
27 pages, 9738 KB  
Article
Machine Learning Recognition and Phase Velocity Estimation of Atmospheric Gravity Waves from OI 557.7 nm All-Sky Airglow Images
by Rady Mahmoud, Moataz Abdelwahab, Kazuo Shiokawa and Ayman Mahrous
AI 2025, 6(10), 262; https://doi.org/10.3390/ai6100262 - 7 Oct 2025
Abstract
Atmospheric gravity waves (AGWs) are treated as density structure perturbations of the atmosphere and play an important role in atmospheric dynamics. Utilizing All-Sky Airglow Imagers (ASAIs) with OI-Filter 557.7 nm, AGW phase velocity and propagation direction were extracted using classified images by visual [...] Read more.
Atmospheric gravity waves (AGWs) are treated as density structure perturbations of the atmosphere and play an important role in atmospheric dynamics. Utilizing All-Sky Airglow Imagers (ASAIs) with OI-Filter 557.7 nm, AGW phase velocity and propagation direction were extracted using classified images by visual inspection, where airglow images were collected from the OMTI network at Shigaraki (34.85 E, 134.11 N) from October 1998 to October 2002. Nonetheless, a large dataset of airglow images are processed and classified for studying AGW seasonal variation in the middle atmosphere. In this article, a machine learning-based approach for image recognition of AGWs from ASAIs is suggested. Consequently, three convolutional neural networks (CNNs), namely AlexNet, GoogLeNet, and ResNet-50, are considered. Out of 13,201 deviated images, 1192 very weak/unclear AGW signatures were eliminated during the quality control process. All networks were trained and tested by 12,007 classified images which approximately cover the maximum solar cycle during the time-period mentioned above. In the testing phase, AlexNet achieved the highest accuracy of 98.41%. Consequently, estimation of AGW zonal and meridional phase velocities in the mesosphere region by a cascade forward neural network (CFNN) is presented. The CFNN was trained and tested based on AGW and neutral wind data. AGW data were extracted from the classified AGW images by event and spectral methods, where wind data were extracted from the Horizontal Wind Model (HWM) as well as the middle and upper atmosphere radar in Shigaraki. As a result, the estimated phase velocities were determined with correlation coefficient (R) above 0.89 in all training and testing phases. Finally, a comparison with the existing studies confirms the accuracy of our proposed approaches in addition to AGW velocity forecasting. Full article
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17 pages, 1178 KB  
Article
A Machine-Learning-Based Prediction Model for Total Glycoalkaloid Accumulation in Yukon Gold Potatoes
by Saipriya Ramalingam, Diksha Singla, Mainak Pal Chowdhury, Michele Konschuh and Chandra Bhan Singh
Foods 2025, 14(19), 3431; https://doi.org/10.3390/foods14193431 - 7 Oct 2025
Abstract
Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. [...] Read more.
Potatoes are the most extensively cultivated vegetable crop in Canada and rank as the fifth largest primary agricultural commodity. Given their diverse end uses and significant market value, particularly in processed forms, ensuring consistent quality from harvest to consumption is of critical importance. Total glycoalkaloids (TGA) are nitrogen-containing secondary metabolites that are known to accumulate in the tuber as an effect of greening in-field or elsewhere in the supply chain. In this study, 210 Yukon Gold (YG) potatoes were exposed to a constant light source to green over a period of 14 days and sampled in 7-day intervals. The samples were scanned using a short-wave infrared (SWIR) hyperspectral imaging camera in the 900–2500 nm wavelength range. Once individually scanned, pixel-wise spectral data was extracted and averaged for each tuber and matched with its respective ground truth TGA values which were obtained using a High-Performance Liquid Chromatography (HPLC) system. Prediction models using the partial least squares regression technique were developed from the extracted hyperspectral data and reference TGA values. Wavelength selection techniques such as competitive adaptive re-weighted sampling (CARS) and backward elimination (BE) were deployed to reduce the number of contributing wavelengths for practical applications. The best model resulted in a correlation coefficient of cross-validation (R2cv) of 0.72 with a root mean square error of cross-validation (RMSEcv) of 51.50 ppm. Full article
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19 pages, 3515 KB  
Article
IR Spectroscopy as a Diagnostic Tool in the Recycling Process and Evaluation of Recycled Polymeric Materials
by Kaiyue Hu, Luigi Brambilla and Chiara Castiglioni
Sensors 2025, 25(19), 6205; https://doi.org/10.3390/s25196205 - 7 Oct 2025
Abstract
Driven by environmental concerns and aligned with the principles of the circular economy, urban plastic waste—including packaging materials, disposable items, non-functional objects, and industrial scrap—is increasingly being collected, recycled, and marketed as a potential substitute for virgin polymers. However, the use of recycled [...] Read more.
Driven by environmental concerns and aligned with the principles of the circular economy, urban plastic waste—including packaging materials, disposable items, non-functional objects, and industrial scrap—is increasingly being collected, recycled, and marketed as a potential substitute for virgin polymers. However, the use of recycled polymers introduces uncertainties that can significantly affect both the durability and the further recyclability of the resulting products. This paper demonstrates how spectroscopic analysis in the mid-infrared (MIR) and near-infrared (NIR) regions can be applied well beyond the basic identification of the main polymeric component, typically performed during the sorting stage of recycling processes. A detailed interpretation of spectral data, based on well-established correlations between spectroscopic response and material structure, enables the classification of recycled polymers according to specific physicochemical properties, such as chemical composition, molecular architecture, and morphology. In this context, infrared spectroscopy not only provides a reliable comparison with the corresponding virgin polymer references but also proves particularly effective in assessing the homogeneity of recycled materials and the reproducibility of their properties—factors not inherently guaranteed due to the variability of input sources. As a case study, we present a robust protocol for determining the polypropylene content in recycled polyethylene samples. Full article
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14 pages, 3118 KB  
Article
Reconstruction Modeling and Validation of Brown Croaker (Miichthys miiuy) Vocalizations Using Wavelet-Based Inversion and Deep Learning
by Sunhyo Kim, Jongwook Choi, Bum-Kyu Kim, Hansoo Kim, Donhyug Kang, Jee Woong Choi, Young Geul Yoon and Sungho Cho
Sensors 2025, 25(19), 6178; https://doi.org/10.3390/s25196178 - 6 Oct 2025
Viewed by 118
Abstract
Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this [...] Read more.
Fish species’ biological vocalizations serve as essential acoustic signatures for passive acoustic monitoring (PAM) and ecological assessments. However, limited availability of high-quality acoustic recordings, particularly for region-specific species like the brown croaker (Miichthys miiuy), hampers data-driven bioacoustic methodology development. In this study, we present a framework for reconstructing brown croaker vocalizations by integrating fk14 wavelet synthesis, PSO-based parameter optimization (with an objective combining correlation and normalized MSE), and deep learning-based validation. Sensitivity analysis using a normalized Bartlett processor identified delay and scale (length) as the most critical parameters, defining valid ranges that maintained waveform similarity above 98%. The reconstructed signals matched measured calls in both time and frequency domains, replicating single-pulse morphology, inter-pulse interval (IPI) distributions, and energy spectral density. Validation with a ResNet-18-based Siamese network produced near-unity cosine similarity (~0.9996) between measured and reconstructed signals. Statistical analyses (95% confidence intervals; residual errors) confirmed faithful preservation of SPL values and minor, biologically plausible IPI variations. Under noisy conditions, similarity decreased as SNR dropped, indicating that environmental noise affects reconstruction fidelity. These results demonstrate that the proposed framework can reliably generate acoustically realistic and morphologically consistent fish vocalizations, even under data-limited scenarios. The methodology holds promise for dataset augmentation, PAM applications, and species-specific call simulation. Future work will extend this framework by using reconstructed signals to train generative models (e.g., GANs, WaveNet), enabling scalable synthesis and supporting real-time adaptive modeling in field monitoring. Full article
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18 pages, 1562 KB  
Article
Adaptive OTFS Frame Design and Resource Allocation for High-Mobility LEO Satellite Communications Based on Multi-Domain Channel Prediction
by Senchao Deng, Zhongliang Deng, Yishan He, Wenliang Lin, Da Wan, Wenjia Wang, Zibo Feng and Zhengdao Fan
Electronics 2025, 14(19), 3939; https://doi.org/10.3390/electronics14193939 - 4 Oct 2025
Viewed by 98
Abstract
In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) [...] Read more.
In Low Earth Orbit (LEO) satellite communication systems, providing reliable data transmission for ultra-high-speed mobile terminals faces severe challenges from dramatic Doppler effects and fast time-varying channels. Orthogonal Time Frequency Space (OTFS) modulation is a promising technique for high-mobility Low Earth Orbit (LEO) satellite communications, but its performance is often limited by inaccurate Channel State Information (CSI) prediction and suboptimal resource allocation, particularly in dynamic channels with coupled parameters like SNR, Doppler, and delay. To address these limitations, this paper proposes an adaptive OTFS frame configuration scheme based on multi-domain channel prediction. We utilize a Long Short-Term Memory (LSTM) network to jointly predict multi-dimensional channel parameters by leveraging their temporal correlations. Based on these predictions, the OTFS transmitter performs two key optimizations: dynamically adjusting the pilot guard bands in the Delay-Doppler domain to reallocate guard resources to data symbols, thereby improving spectral efficiency while maintaining channel estimation accuracy; and performing optimal power allocation based on predicted sub-channel SNRs to minimize the system’s Bit Error Rate (BER). The simulation results show that our proposed scheme reduces the required SNR for a BER of 1×103 by approximately 1.5 dB and improves spectral efficiency by 10.5% compared to baseline methods, demonstrating its robustness and superiority in high-mobility satellite communication scenarios. Full article
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15 pages, 2125 KB  
Article
Surface Mapping by RPAs for Ballast Optimization and Slip Reduction in Plowing Operations
by Lucas Santos Santana, Lucas Gabryel Maciel do Santos, Josiane Maria da Silva, Aldir Carpes Marques Filho, Francesco Toscano, Enio Farias de França e Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Thieres George Freire da Silva and Marco Antonio Zanella
AgriEngineering 2025, 7(10), 332; https://doi.org/10.3390/agriengineering7100332 - 3 Oct 2025
Viewed by 226
Abstract
Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating [...] Read more.
Driving wheel slippage in agricultural tractors is influenced by soil moisture, density, and penetration resistance. These surface variations reflect post-tillage composition, enabling dynamic mapping via Remotely Piloted Aircraft (RPAs). This study evaluated ballast recommendations based on soil surface data and slippage percentages, correlating added wheel weights at different speeds for a tractor-reversible plow system. Six 94.5 m2 quadrants were analyzed for slippage monitored by RPA (Mavic3M-RTK) pre- and post-agricultural operation overflights and soil sampling (moisture, density, penetration resistance). A 2 × 2 factorial scheme (F-test) assessed soil-surface attribute correlations and slippage under varying ballasts (52.5–57.5 kg/hp) and speeds. Results showed slippage ranged from 4.06% (52.5 kg/hp, fourth reduced gear) to 11.32% (57.5 kg/hp, same gear), with liquid ballast and gear selection significantly impacting performance in friable clayey soil. Digital Elevation Model (DEM) and spectral indices derived from RPA imagery, including Normalized Difference Red Edge (NDRE), Normalized Difference Water Index (NDWI), Bare Soil Index (BSI), Green–Red Vegetation Index (GRVI), Visible Atmospherically Resistant Index (VARI), and Slope, proved effective. The approach reduced tractor slippage from 11.32% (heavy ballast, 4th gear) to 4.06% (moderate ballast, 4th gear), showing clear improvement in traction performance. The integration of indices and slope metrics supported ballast adjustment strategies, particularly for secondary plowing operations, contributing to improved traction performance and overall operational efficiency. Full article
(This article belongs to the Special Issue Utilization and Development of Tractors in Agriculture)
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14 pages, 2518 KB  
Article
Assessment of Intervertebral Lumbar Disk Herniation: Accuracy of Dual-Energy CT Compared to MRI
by Giuseppe Ocello, Gianluca Tripodi, Flavio Spoto, Leonardo Monterubbiano, Gerardo Serra, Giorgio Merci and Giovanni Foti
J. Clin. Med. 2025, 14(19), 7000; https://doi.org/10.3390/jcm14197000 - 3 Oct 2025
Viewed by 175
Abstract
Background: Lumbar disk herniation is a common cause of low back pain and radiculopathy, significantly impacting patients’ life quality and functional capacity. Magnetic Resonance Imaging (MRI) remains the gold standard for its assessment due to its superior soft tissue contrast and multiplanar imaging [...] Read more.
Background: Lumbar disk herniation is a common cause of low back pain and radiculopathy, significantly impacting patients’ life quality and functional capacity. Magnetic Resonance Imaging (MRI) remains the gold standard for its assessment due to its superior soft tissue contrast and multiplanar imaging capabilities. However, recent advances in spectral computed tomography (CT), particularly dual-energy CT (DECT), have introduced new diagnostic opportunities, offering improved soft tissue characterization. Objective: To evaluate the diagnostic performance of DECT in detecting and grading lumbar disk herniations using dedicated color-coded fat maps. Materials and Methods: A total of 205 intervertebral levels from 41 consecutive patients with lumbar symptoms were prospectively analyzed. All patients underwent both DECT and MRI within 3 days. Three radiologists with varying years of experience independently assessed DECT images using color-coded reconstructions. A five-point grading score was attributed to each lumbar level: 1 = normal disk, 2 = bulging/protrusion, 3 = focal herniation, 4 = extruded herniation, and 5 = migrated fragment. The statistical analysis included Pearson’s correlation for score consistency, Cohen’s Kappa for interobserver agreement, generalized estimating equations for a cluster-robust analysis, and an ROC curve analysis. The DECT diagnostic accuracy was assessed in a dichotomized model (grades 1–2 = no herniation; 3–5 = herniation), using MRI as reference. Results: A strong correlation was observed between DECT and MRI scores across all readers (mean Pearson’s r = 0.826, p < 0.001). The average exact agreement between DECT and MRI was 79.4%, with the highest concordance at L1–L2 (86.7%) and L5–S1 (80.4%). The interobserver agreement was substantial (mean Cohen’s κ = 0.765), with a near-perfect agreement between the two most experienced readers (κ = 0.822). The intraclass correlation coefficient was 0.906 (95% CI: 0.893–0.918). The ROC analysis showed excellent performance (AUC range: 0.953–0.986). In the dichotomous model, DECT demonstrated a markedly higher sensitivity than conventional CT (95.1% vs. 57.2%), with a comparable specificity (DECT: 99.0%; CT: 96.5%) and improved overall accuracy (98.4% vs. 90.0%). Subgroup analyses by age and disk location revealed no statistically significant differences. Conclusions: The use of DECT dedicated color-coded fat map reconstructions showed high diagnostic performance in the assessment of lumbar disk herniations compared to MRI. These findings support the development of dedicated post-processing tools, facilitating the broader clinical adoption of spectral CT, especially in cases where MRI is contraindicated or less accessible. Full article
(This article belongs to the Special Issue Dual-Energy and Spectral CT in Clinical Practice: 2nd Edition)
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20 pages, 33056 KB  
Article
Spatiotemporal Analysis of Vineyard Dynamics: UAS-Based Monitoring at the Individual Vine Scale
by Stefan Ruess, Gernot Paulus and Stefan Lang
Remote Sens. 2025, 17(19), 3354; https://doi.org/10.3390/rs17193354 - 2 Oct 2025
Viewed by 221
Abstract
The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and [...] Read more.
The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and Object-Based Image Analysis (OBIA) to detect and monitor individual grapevines throughout the growing season. Vines are identified directly from 3D point clouds without the need for prior training data or predefined row structures, achieving a mean Euclidean distance of 10.7 cm to the reference points. The OBIA framework segments vine vegetation based on spectral and geometric features without requiring pre-clipping or manual masking. All non-vine elements—including soil, grass, and infrastructure—are automatically excluded, and detailed canopy masks are created for each plant. Vegetation indices are computed exclusively from vine canopy objects, ensuring that soil signals and internal canopy gaps do not bias the results. This enables accurate per-vine assessment of vigour. NDRE values were calculated at three phenological stages—flowering, veraison, and harvest—and analyzed using Local Indicators of Spatial Association (LISA) to detect spatial clusters and outliers. In contrast to value-based clustering methods, LISA accounts for spatial continuity and neighborhood effects, allowing the detection of stable low-vigour zones, expanding high-vigour clusters, and early identification of isolated stressed vines. A strong correlation (R2 = 0.73) between per-vine NDRE values and actual yield demonstrates that NDRE-derived vigour reliably reflects vine productivity. The method provides a transferable, data-driven framework for site-specific vineyard management, enabling timely interventions at the individual plant level before stress propagates spatially. Full article
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24 pages, 22010 KB  
Article
Improving the Temporal Resolution of Land Surface Temperature Using Machine and Deep Learning Models
by Mohsen Niroomand, Parham Pahlavani, Behnaz Bigdeli and Omid Ghorbanzadeh
Geomatics 2025, 5(4), 50; https://doi.org/10.3390/geomatics5040050 - 1 Oct 2025
Viewed by 250
Abstract
Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 [...] Read more.
Land Surface Temperature (LST) is a critical parameter for analyzing urban heat islands, surface–atmosphere interactions, and environmental management. This study enhances the temporal resolution of LST data by leveraging machine learning and deep learning models. A novel methodology was developed using Landsat 8 thermal data and Sentinel-2 multispectral imagery to predict LST at finer temporal intervals in an urban setting. Although Sentinel-2 lacks a thermal band, its high-resolution multispectral data, when integrated with Landsat 8 thermal observations, provide valuable complementary information for LST estimation. Several models were employed for LST prediction, including Random Forest Regression (RFR), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Gated Recurrent Unit (GRU). Model performance was assessed using the coefficient of determination (R2) and Mean Absolute Error (MAE). The CNN model demonstrated the highest predictive capability, achieving an R2 of 74.81% and an MAE of 1.588 °C. Feature importance analysis highlighted the role of spectral bands, spectral indices, topographic parameters, and land cover data in capturing the dynamic complexity of LST variations and directional patterns. A refined CNN model, trained with the features exhibiting the highest correlation with the reference LST, achieved an improved R2 of 84.48% and an MAE of 1.19 °C. These results underscore the importance of a comprehensive analysis of the factors influencing LST, as well as the need to consider the specific characteristics of the study area. Additionally, a modified TsHARP approach was applied to enhance spatial resolution, though its accuracy remained lower than that of the CNN model. The study was conducted in Tehran, a rapidly urbanizing metropolis facing rising temperatures, heavy traffic congestion, rapid horizontal expansion, and low energy efficiency. The findings contribute to urban environmental management by providing high-temporal-resolution LST data, essential for mitigating urban heat islands and improving climate resilience. Full article
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12 pages, 872 KB  
Article
Integrating Machine Learning and Molecular Methods for Trichophyton indotineae Identification and Resistance Profiling Using MALDI-TOF Spectra
by Vittorio Ivagnes, Elena De Carolis, Carlotta Magrì, Manuel J. Arroyo, Giacomina Pavan, Anna Cristina Maria Prigitano, Anuradha Chowdhary and Maurizio Sanguinetti
Pathogens 2025, 14(10), 986; https://doi.org/10.3390/pathogens14100986 - 30 Sep 2025
Viewed by 309
Abstract
Trichophyton indotineae is an emerging dermatophyte species responsible for recalcitrant and terbinafine-resistant dermatophytosis, raising concerns over diagnostic accuracy and treatment efficacy. This study aimed to improve the identification and resistance profiling of T. indotineae by integrating molecular methods with machine learning-assisted analysis of [...] Read more.
Trichophyton indotineae is an emerging dermatophyte species responsible for recalcitrant and terbinafine-resistant dermatophytosis, raising concerns over diagnostic accuracy and treatment efficacy. This study aimed to improve the identification and resistance profiling of T. indotineae by integrating molecular methods with machine learning-assisted analysis of MALDI-TOF mass spectra. A total of 56 clinical isolates within the Trichophyton mentagrophytes complex were analyzed using ITS and ERG1 gene sequencing, antifungal susceptibility testing, and MALDI-TOF MS profiling. Terbinafine resistance was detected in 23 isolates and correlated with specific ERG1 mutations, including F397L, L393S, F415C, and A448T. While conventional MALDI-TOF MS failed to reliably distinguish T. indotineae from closely related species, unsupervised statistical methods (PCA and hierarchical clustering) revealed distinct spectral groupings. Supervised machine learning algorithms, particularly PLS-DA and SVM, achieved 100% balanced accuracy in species classification using 10-fold cross-validation. Biomarker analysis identified discriminatory spectral peaks for both T. indotineae and T. mentagrophytes (3417.29 m/z and 3423.53 m/z). These results demonstrate that combining MALDI-TOF MS with multivariate analysis and machine learning improves diagnostic resolution and may offer a practical alternative to sequencing in resource-limited settings. This approach could enhance the routine detection of terbinafine-resistant T. indotineae and support more targeted antifungal therapy. Full article
(This article belongs to the Special Issue Epidemiology and Molecular Detection of Emerging Fungal Pathogens)
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36 pages, 20880 KB  
Article
NDGRI: A Novel Sentinel-2 Normalized Difference Gamma-Radiation Index for Pixel-Level Detection of Elevated Gamma Radiation
by Marko Simić, Boris Vakanjac and Siniša Drobnjak
Remote Sens. 2025, 17(19), 3331; https://doi.org/10.3390/rs17193331 - 29 Sep 2025
Viewed by 269
Abstract
This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi-arid and arid basins. We hypothesized that water-sensitive spectral indices correlate with gamma-ray hotspots in arid regions [...] Read more.
This study introduces the Normalized Difference Gamma Ray Index (NDGRI), a novel spectral composite derived from Sentinel 2 imagery for mapping elevated natural gamma radiation in semi-arid and arid basins. We hypothesized that water-sensitive spectral indices correlate with gamma-ray hotspots in arid regions of Mongolia, where natural radionuclide distribution is influenced by hydrological processes. Leveraging historical car-borne gamma spectrometry data collected in 2008 across the Sainshand and Zuunbayan uranium project areas, we evaluated twelve spectral bands and five established moisture-sensitive indices against radiation heatmaps in Naarst and Zuunbayan. Using Pearson and Spearman correlations alongside two percentile-based overlap metrics, indices were weighted to yield a composite performance score. The best performing indices (MI—Moisture Index and NDSII_1—Normalized Difference Snow and Ice Index) guided the derivation of ten new ND constructs incorporating SWIR bands (B11, B12) and visible bands (B4, B8A). The top performer, NDGRI = (B4 − B12)/(B4 + B12) achieved a precision of 62.8% for detecting high gamma-radiation areas and outperformed benchmarks of other indices. We established climatological screening criteria to ensure NDGRI reliability. Validation at two independent sites (Erdene, Khuvsgul) using 2008 airborne gamma ray heatmaps yielded 76.41% and 85.55% spatial overlap accuracy, respectively. Our results demonstrate that NDGRI effectively delineates gamma radiation hotspots where moisture-controlled spectral contrasts prevail. The index’s stringent acquisition constraints, however, limit the temporal availability of usable scenes. NDGRI offers a rapid, cost-effective remote sensing tool to prioritize ground surveys in uranium prospective basins and may be adapted for other radiometric applications in semi-arid and arid regions. Full article
(This article belongs to the Special Issue Remote Sensing in Engineering Geology (Third Edition))
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22 pages, 2558 KB  
Article
Spectral Derivatives Improve FTIR-Based Machine Learning Classification of Plastic Polymers
by Octavio Rosales-Martínez, Everardo Efrén Granda-Gutiérrez, René Arnulfo García-Hernández, Roberto Alejo-Eleuterio and Allan Antonio Flores-Fuentes
Modelling 2025, 6(4), 115; https://doi.org/10.3390/modelling6040115 - 29 Sep 2025
Viewed by 650
Abstract
Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- [...] Read more.
Accurate identification of plastic polymers is essential for effective recycling, quality control, and environmental monitoring. This study assesses how spectral derivative preprocessing affects the classification of six common plastic polymers: Polyethylene Terephthalate (PET), Polyvinyl Chloride (PVC), Polypropylene (PP), Polystyrene (PS), and both High- and Low-Density Polyethylene (HDPE and LDPE), based on Fourier Transform Infrared (FTIR) spectroscopy data acquired at a resolution of 8 cm1. Using Savitzky–Golay derivatives (orders 0, 1, and 2), five machine learning algorithms, namely Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Linear Discriminant Analysis (LDA), Support Vector Classifier (SVC), and Random Forest (RF), were tested within a strict framework involving stratified repeated cross-validation and a final hold-out test set to evaluate generalization. The first spectral derivative notably improved the model performance, especially for MLP and SVC, and increased the stability of the ET, LDA, and RF classifiers. The combination of the first derivative with the ET model provided the best results, achieving a mean F1-score of 0.99995 (±0.00033) in cross-validation and perfect classification (1.0 in Accuracy, F1-score, Cohen’s Kappa, and Matthews Correlation Coefficient) on the independent test set. LDA also performed very well, underscoring the near-linear separability of spectral data after derivative transformation. These results demonstrate the value of derivative-based preprocessing and confirm a robust method for creating high-precision, interpretable, and transferable machine learning models for automated plastic polymer identification. Full article
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20 pages, 1724 KB  
Article
Spectral Features of Wolaytta Ejectives
by Firew Elias, Derib Ado and Feda Negesse
Languages 2025, 10(10), 250; https://doi.org/10.3390/languages10100250 - 29 Sep 2025
Viewed by 353
Abstract
This study analyzes the spectral properties of word-initial and intervocalic ejectives in Wolaytta, an Omotic language of southern Ethiopia. Using tokens embedded in three vowel contexts, we examined mean burst intensity, spectral moments, and vowel perturbation following ejection. Results show that ejectives adjacent [...] Read more.
This study analyzes the spectral properties of word-initial and intervocalic ejectives in Wolaytta, an Omotic language of southern Ethiopia. Using tokens embedded in three vowel contexts, we examined mean burst intensity, spectral moments, and vowel perturbation following ejection. Results show that ejectives adjacent to high front vowels were produced with greater intensity, supporting the hypothesis that increased oral cavity tenseness correlates with acoustic energy. Centroid and standard deviation differentiate place of articulation, while skewness and kurtosis distinguish singleton from geminate ejectives. Post-ejective vowel pitch and spectral tilt varied systematically with the ejectives’ place of articulation, indicating creaky phonation induced by ejection. Overall, the findings enhance our understanding of factors impacting acoustic features of ejectives. Full article
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33 pages, 10753 KB  
Article
Spectral Analysis of Snow in Bansko, Pirin Mountain, in Different Ranges of the Electromagnetic Spectrum
by Temenuzhka Spasova, Andrey Stoyanov, Adlin Dancheva and Daniela Avetisyan
Remote Sens. 2025, 17(19), 3326; https://doi.org/10.3390/rs17193326 - 28 Sep 2025
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Abstract
The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is [...] Read more.
The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article
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