Search Results (635)

Accurately tracking the spatial and temporal variations of water vapor is indispensable for weather forecasting and climate adaptation, yet remains challenging due to the sparse coverage and discontinuity of ground-based observations. Satellite remote sensing, particularly from geostationary satellites like Meteosat Third Generation Imager-1 (MTG-I1), offers continuous, high-resolution data. To the best of our knowledge, MTG-I1 is the first geostationary satellite equipped with a near-infrared (NIR) spectral band specifically designed for detecting water vapor. To address the lack of precipitable water vapor (PWV) data derived from the Flexible Combined Imager (FCI) onboard MTG-I1, a novel semi-empirical (SE) algorithm optimized for PWV retrieval is proposed. Validation against ground-based PWV measurements using an initial test set and a temporally independent test set yielded relative errors of no more than 0.10, indicating stable retrieval performance outside the model-development period. The FCI-derived PWV retrievals were also more accurate than the corresponding MODIS PWV data. Compared to the traditional radiative transfer model (RTM)-based retrieval method, the SE method shows greater adaptability to systematic differences between the observed and RTM-simulated FCI reflectance. After correcting for radiometric degradation, the RTM-based algorithm achieves a 41% reduction in absolute error and a 47% reduction in relative error, bringing its accuracy in line with the SE algorithm. Overall, the proposed SE algorithm demonstrates superior robustness and adaptability, and can provide more reliable remote sensing PWV data to support weather forecasting and climate research. Full article

Background: Near-infrared spectroscopy (NIRS)-based wearable devices offer non-invasive, continuous monitoring of muscle oxygenation, providing direct microvascular and metabolic information that complements indirect indices of intensity such as heart rate and accelerometry. Their clinical applicability in chronic non-communicable diseases (NCDs) remains under active development. Methods: A structured narrative review was conducted in PubMed, Scopus, Web of Science, and IEEE Xplore (January 2010–January 2026) using pre-specified search strings combining NIRS, muscle oxygenation, SmO₂, StO₂, wearable, exercise intensity, ventilatory/lactate threshold, and individual chronic disease terms. Eligible studies addressed technical validation of wearable NIRS, NIRS-derived exercise intensity estimation, clinical applications in NCDs, or rehabilitation implementation. Evidence was synthesized thematically; quality of validation studies was appraised against AMSTAR-2-informed, COSMIN-informed, or Cochrane RoB-2 criteria. Results: Wearable continuous-wave NIRS shows acceptable concurrent validity with frequency-domain laboratory systems (r = 0.79; range 0.69–0.88; ±8% SmO₂ agreement in 95% of measurements) and good test–retest reliability for moderate-to-severe domains (ICC 0.72–0.91). NIRS-derived breakpoints align more reliably with the second ventilatory/lactate threshold (ICC = 0.80) than with the first (ICC = 0.53), constraining its use for prescribing lower-intensity domains. In chronic obstructive pulmonary disease, peripheral arterial disease, chronic respiratory failure and selected cardiovascular conditions, wearable NIRS detects disease-specific patterns of muscle deoxygenation and post-exercise reoxygenation that track responses to rehabilitation. Conclusions: Current evidence supports wearable NIRS as a complementary, intensity-aware monitoring tool—particularly for delineating the heavy/severe-intensity boundary and detecting peripheral metabolic limitations—rather than as a stand-alone replacement for ventilatory or lactate thresholds. Because much of the evidence derives from small, single-sex or athlete-only cohorts, these findings should be regarded as a promising basis requiring further validation in broader NCD populations. Implementation in NCDs requires standardized placement and calibration protocols, sex- and body composition-stratified reference values, motion-artifact mitigation, and adequately powered longitudinal trials in clinical populations. Full article

Lack of fusion (LOF) is a dominant defect in Laser Powder-Bed Fusion (PBF-LB/M) caused by insufficient overlapping between adjacent melt pools. This study introduces a rapid, first-principles model based on Rosenthal’s analytical solution for a moving point heat source to predict melt pool geometry. Using geometric criteria, the model evaluates whether the melt pool width exceeds the hatching distance and whether the melt pool depth exceeds the layer thickness. Based on these conditions, LOF-based process windows are constructed by plotting laser power against scanning speed and classifying each parameter combination as either LOF or no LOF. The process developed here for constructing LOF process windows can be applied to metallic PBF-LB/M systems. As PBF-LB/M of copper is commonly associated with LOF defects, the approach is examined for pure copper by evaluating a range of laser powers and scanning speeds for both near-infrared (NIR) (1064 nm) and green (515 nm) lasers using copper-specific absorptivity values. The resulting process windows are validated against literature-reported relative density data for pure copper, using high relative density values as indicators of full fusion and lower relative density values reported with LOF characteristics as indicators of lack of fusion. For a 30 µm layer thickness, the predicted LOF boundary agreed with 43 of 46 literature-reported copper PBF-LB/M data points when the data were classified using relative density and reported defect morphology. Sensitivity analysis showed that the agreement changed modestly when the relative-density threshold was reduced from 99% to 98.5% and 98% and that near-boundary classifications were sensitive to the selected absorptivity within the reported NIR range. The agreement supports the use of the framework as a preliminary screening tool for identifying LOF-prone parameter regions. By providing a fast, physics-based screening tool for LOF-limited process windows, this framework offers a computationally efficient alternative to high-fidelity numerical simulations commonly used in PBF-LB/M process development. Full article

In Italy, the apple cider market is experiencing significant growth, driven by numerous small-scale artisanal producers who combine local apple varieties with traditional processes to offer complex, and diverse products. However, artisanal production based on spontaneous fermentations often encounters challenges in qualitative reproducibility, particularly related to sensory issues (stability across different vintages and high turbidity of the product). In this context, a methodology has been developed to optimize the technological process of cider production at Contrada Contro in the Monti Sibillini (MC), in Marche region, Italy. The research focused on the isolation and selection of indigenous yeasts from frozen must prepared in the 2023 vintage. Following isolation and preliminary characterization, the indigenous yeasts were used to referment the still cider, followed by 7 months of bottle aging, and a second sampling point was conducted after 14 months of aging on lees. Destructive analyses using HPLC-DAD and GC-MS were conducted to evaluate polyphenols and volatile compounds, while non-destructive analyses with a 12-quartz microbalance electronic nose and near infrared (NIR) spectroscopy allowed for a quicker assessment of production techniques. Chromatographic analysis results showed that the sensory quality of refermented products was strongly influenced by the composition of the yeast strains used. All fermentations inoculated with selected yeasts exhibited lower turbidity compared to spontaneous fermentation. These findings indicate that the selection of indigenous yeasts for cider refermentation enables the production of a high-quality product, enriched with beneficial compounds and characterized by a strong terroir identity, underscoring the importance of microbiological terroir. Full article

Rapid and effective estimation of soil organic matter (SOM) is crucial for the scientific management of Moso bamboo forests. This study investigated Moso bamboo forest soils in Yongan City, Fujian Province, and systematically evaluated the synergistic adaptation strategies coupling spectral preprocessing methods, feature extraction strategies, and machine learning models based on visible and shortwave near-infrared (Vis-NIR) spectroscopy. The results indicated that: (1) Conventional preprocessing algorithms attenuated the SOM spectral feature signals dominated by soil color within the limited wavelength range of field in situ spectral data, resulting in a general decline in the accuracy of the estimation models. (2) Feature extraction and modeling algorithms exhibited distinct adaptability across different content intervals. Within the low-content interval (<15 g/kg), simple physical indices combined with random forest (RF) achieved effective estimation at a lower computational cost (RPD = 2.18). Within the high-content interval (>25 g/kg), the synergistic strategy of the CARS algorithm combined with support vector regression (SVR) yielded the optimal estimation performance (R² = 0.83, RPD = 2.45) and effectively mitigated the underestimation of high values caused by data imbalance. In conclusion, this study proposed a feature–model synergistic estimation approach, validating its feasibility for estimating SOM in Moso bamboo forests under the specific constraints of the current study area, thereby serving as a valuable reference for forest soil SOM monitoring in specific regions. Full article

Near-infrared spectroscopy (NIRS) is widely used for rapid and non-destructive evaluation of feed nutritional quality, but robust calibration remains challenging for heterogeneous multi-product feed datasets. This study evaluated convolutional neural network (CNN)-based models for predicting crude protein (CP) and acid detergent fiber (ADF) using a previously published NIR database containing forage and grain-based feeds. A one-dimensional CNN and two hybrid models, CNN combined with partial least squares regression (CNN+PLS) and XGBoost (CNN+XGBoost), were developed and compared with conventional PLSR calibration models based on either the pooled multi-product dataset or product-specific subsets. Model performance was assessed using an independent internal hold-out test set generated within the same database. For CP prediction, CNN-based models achieved strong performance on the hold-out test set, with testing R² values of 0.98 and RMSEP values of 0.60–0.62, showing a clear reduction in prediction error compared with the global PLSR model. For ADF, CNN and CNN+PLS provided only modest improvements over global PLSR, whereas CNN+XGBoost showed weaker generalization for ADF. Product-wise results further indicated that ADF prediction was more strongly affected by feed matrix and product category than CP prediction. Grad-CAM examples suggested that CNN activation patterns were broadly consistent with known protein- and fiber-related absorption regions, although this interpretation should be regarded as illustrative evidence of spectral coherence rather than direct chemical causality. Overall, CNN-based models, particularly CNN+PLS, showed promise for improving NIRS prediction of CP in heterogeneous feed datasets, while their advantage for ADF was limited. Further validation using independent external datasets and multi-instrument conditions is required before routine implementation. Full article

Cardiopulmonary exercise testing (CPET) is the reference method for the objective assessment of exercise capacity because it provides an integrated appraisal of cardiovascular, respiratory and metabolic responses to exertion. However, CPET alone quantifies the magnitude of functional impairment without fully resolving the central and peripheral mechanisms that determine exercise intolerance. The integration of CPET with exercise stress echocardiography and near-infrared spectroscopy (NIRS) has therefore emerged as a clinically relevant multimodal strategy. Stress echocardiography provides real-time information on ventricular reserve, filling pressures, pulmonary pressure response, valvular function, pulmonary congestion and dynamic outflow obstruction, whereas NIRS provides continuous insight into skeletal muscle oxygen delivery, extraction and utilization. This narrative review summarizes the physiological rationale, practical workflow, methodological limitations and clinical applications of combined CPET, stress echocardiography and NIRS across heart failure, pulmonary hypertension, peripheral artery disease, cardiomyopathies and sports cardiology. By linking systemic gas exchange, central hemodynamics and peripheral oxygen handling, this approach may move exercise evaluation from a descriptive measure of performance toward a mechanism-based framework for phenotyping, risk stratification and individualized therapeutic decision-making. Further studies are needed to harmonize protocols, validate reproducible multimodal indices and demonstrate incremental prognostic value over conventional testing. Full article

Fatty acid abundance is a key parameter for evaluating the quality of edible oils. This study developed a rapid and non-destructive method for predicting palmitic acid content in edible oils by combining visible-near-infrared (Vis-NIR) reflectance spectroscopy with deep learning models. A total of 1740 reflectance spectra in the range of 350–2500 nm were collected from 87 brands of edible oils, including peanut, soybean, corn, sunflower, rapeseed, sesame, and olive oils. Reference values of palmitic acid content were determined via gas chromatography–mass spectrometry (GC-MS). Two conventional machine learning models (SVR and KNN) and four deep learning models (1D-CNN, 1D-ResNet, 1D-Inception, and 1D-Inception-ResNet) were developed and compared using both full-spectrum data and CARS selected characteristic wavelengths. Among the full-spectrum models, the designed 1D-ResNet model achieved the best performance, with the determination coefficient of prediction ($R_p^2$) of 0.9027 and the root mean square error of prediction ($RMS{E}_p$) of 1.13 in the prediction dataset. The proposed 1D-Inception-ResNet model yielded the best prediction results based on the 91 selected informative wavelengths via competitive adaptive reweighted sampling (CARS), achieving an $R_p^2$ of 0.9825 and an $RMS{E}_p$ of 0.4804 in the prediction dataset. The experimental results indicated that Vis-NIR reflectance spectroscopy combined with informative wavelength selection and deep learning models provided an effective strategy for rapid prediction of palmitic acid content in edible oils. Full article

Postharvest quality of carrot (Daucus carota L.) is determined by the interaction between genotype and storage environment, yet systematic comparative evidence across pigmented genotypes with contrasting biochemical profiles remains scarce. This study evaluated the postharvest behavior of five carrot genotypes (6KUR, 14BER, yellow, white, and purple) under refrigeration (4 °C) and room temperature (15 °C) over 30 days, integrating physicochemical, spectral, and consumer-based assessment. Variables included color, fresh weight loss, respiration rate, firmness, titratable acidity, total soluble solids, and β-carotene quantification by spectrophotometry. Non-destructive monitoring was performed using Vis/NIR reflectance (350–1900 nm) with spectral indices sensitive to anthocyanin and carotenoid content (CRI1, CRI2, mARI) and tissue structural integrity (NDVI), and consumer perception (~60 participants per evaluation) was characterized through natural language processing of open-ended responses. Refrigeration significantly reduced β-carotene degradation (~15–20% loss vs. 50–60% at room temperature) and better preserved overall quality across genotypes. Purple carrots demonstrated superior postharvest stability across multiple traits, whereas white carrots showed the greatest susceptibility to quality loss. Spectral indices exhibited genotype-dependent temporal variation, particularly in pigmented roots, supporting their potential for non-destructive pigment monitoring during storage. Consumer descriptors reflected a progressive decline in desirable sensory attributes under both conditions. These findings support the integration of physicochemical, spectral, and sensory approaches for comprehensive postharvest characterization of genotypically diverse carrot germplasm, and identify priority genotypes and trait combinations for future predictive modeling studies. Full article

Background/Objectives: Functional near-infrared spectroscopy (fNIRS) has emerged as a non-invasive, implant-compatible imaging modality capable of capturing cortical hemodynamics during ecologically valid auditory and linguistic tasks. Its silent operation and tolerance to electrical artifacts make it particularly well suited to the study of hearing-impaired individuals, including cochlear implant (CI) users. However, evidence on the application of fNIRS to investigate speech perception, cognitive performance, and proxy of cortical activation patterns in patients with hearing loss (HL) remains fragmented. This systematic review aims to provide a structured, population-stratified description of current fNIRS literature on auditory and cognitive processing in adults with age-related hearing loss (ARHL) and CI users. Methods: A systematic search on PubMed Central, Web of Science and Scopus, based on PRISMA (2020) guidelines, was conducted to identify original studies that evaluate speech perception by means of fNIRS to assess auditory and cognitive process in hearing-impaired populations. Results: Across studies, fNIRS consistently detected activation of superior temporal and frontal cortices during speech-related tasks. In ARHL, increased dorsolateral prefrontal cortex (DLPFC) recruitment during speech-in-noise indicated compensatory yet inefficient processing. Longitudinal auditory training led to reduced prefrontal overactivation and enhanced temporal–frontal connectivity. In CI users, cortical responses to phonological and comprehension tasks show partially overlapping activation patterns with normal hearing (NH) peers, although arising within different neurobiological contexts, and are modulated by device experience and residual hearing (AV) speech, and stimulus-level effects further shape cortical responses. When interpreted in light of developmental evidence, these findings may be contextualized as reflecting distinct trajectories of cortical reorganization, rather than a common mechanism. Conclusions: fNIRS provides a tool to investigate auditory and cognitive responses in distinct hearing-impaired populations under ecologically valid conditions. It detects maladaptive frontal inefficiency in ARHL, tracks neuroplastic changes after rehabilitation, and captures population-specific cortical recruitment patterns in CI users. These findings are descriptive and context-dependent, and do not support cross-population mechanistic generalizations. Standardized protocols and longitudinal pediatric studies are needed to clarify the potential clinical relevance of fNIRS-derived cortical measures. Full article

The aim of this study was to examine the potential of multispectral imaging derived from unmanned aerial vehicles (UAVs) for detecting the spread of mistletoe (Viscum album ssp. austriacum L.) in Scots pine stands and to assess the information potential of selected vegetation indices in mistletoe detection. UAV campaigns were performed in the Niepołomice Primeval Forest (Niepołomice Forest District, Regional Directorate of the Polish State Forests National Holding, Kraków, Poland). A fixed-wing UAV Trinity F90+ (Quantum Systems GmbH) equipped with a five-band multispectral MicaSense RedEdge-M camera and an RGB Sony UMC-R10C camera was employed. The number of trees infected by mistletoe, as well as the quantity and area of mistletoe biogroups, were derived based on the classification of true multispectral orthophotos using a support vector machine (SVM) classifier. The spectral information potential assessment identified NIR (B5) as the most important single spectral source of information, while the greatest information potential among vegetation indices was found in NormG, CIG, and GRVI. The mistletoe classification of the 22.5-ha compartment revealed 1735 mistletoe biogroups covering a total area of 489 m², with 58.6% of the 2917 detected tree crowns identified as infected (Kappa = 0.74). The results confirm that UAV-based multispectral data, particularly when combined with green-sensitive vegetation indices, enable effective differentiation of mistletoe from host tree crowns. The integration of the near-infrared (NIR) band further enhanced classification performance. This study evaluates UAV-based multispectral and RGB imagery for detecting common mistletoe (Viscum album ssp. austriacum) in Scots pine stands. The information potential of 22 vegetation indices was assessed to identify the most effective spectral features for mistletoe classification. Full article

The accurate estimation of pasture above-ground biomass (AGB) is critical for optimizing stocking rates and ensuring the sustainable use of Mediterranean pastures. This study developed empirical models to estimate fresh (AGB_fresh) and dry above-ground biomass (AGB_dry) using multispectral imagery acquired by Unmanned Aerial Vehicles (UAVs) in a Hedysarum coronarium pasture in Sicily, Italy. Field biomass was destructively sampled simultaneously with UAV surveys in 28 georeferenced plots during pre- and post-grazing phases over the 2023–2024 and 2024–2025 seasons. Data were collected with a DJI Mavic 3 Multispectral (for the 2024 test) and a DJI Matrice 300 + Altum-PT (for the 2025 test) and radiometrically calibrated to surface reflectance. Because two different multispectral sensors were used across years, an inter-sensor harmonization step was applied before vegetation-index calculation. Thirty-three vegetation indices were extracted as mean values within circular buffers of 1 m radius, centered on each sample plot to accommodate GNSS/georeferencing uncertainty. For each vegetation index, linear and exponential models were calibrated using 66% of the dataset and validated on the remaining 33% to predict fresh and dry above-ground biomass, and model performance was assessed using R² and RMSE. On the validation dataset, ARVI2 and EVI2 showed the highest explanatory power for AGB_fresh (R² = 0.89), with ARVI2 providing the lower RMSE (2047 g m⁻²). For AGB_dry, visible-band indices such as NGRDI and GRVI were among the best performers, reaching R² = 0.85 with RMSE = 1371 g m⁻². Visible-band greenness indices were among the most competitive predictors, whereas several conventional NIR-based indices showed only moderate performance. Overall, this UAV-based multispectral approach represents a promising and interpretable tool for biomass estimation in heterogeneous Mediterranean pastures, although further validation across additional seasons and sites is required to strengthen its transferability. Full article

This study investigated the cortical activation patterns and functional connectivity underlying human decision-making by comparing two distinct purchasing orientations: other-oriented consumption (OOC) and self-oriented consumption (SOC), using functional near-infrared spectroscopy (fNIRS) as a wearable neuroimaging modality. The results revealed significant temporal concentration differences in $∆\mathrm{H}\mathrm{b}\mathrm{O}$ under the OOC condition in Ch06 ($p$< 0.05). The 15 fNIRS channels were mapped to seven anatomically defined regions of interest ($\mathrm{ROIs}$) to better capture regional activation patterns and functional network properties. While global network metrics showed no significant differences, seed-based connectivity analysis revealed that the OOC condition elicited significantly stronger functional connectivity between the medial prefrontal cortex (${\mathrm{R}\mathrm{O}\mathrm{I}}_4$) and the left lower PFC (${\mathrm{R}\mathrm{O}\mathrm{I}}_6$, $p$ < 0.05, $d$ = 0.45). In summary, while the overall network efficiency remained stable across conditions, our findings highlight a spatially specific enhancement in functional connectivity centered on the PFC, indicating an increased cognitive load from engaging in complex social cognitive processes. These findings advance the understanding of neural correlates underlying human decision-making and demonstrate the utility of wearable monitoring using fNIRS for capturing cognitive state differences in human-centered decision contexts. Full article

This study examines the impact of biophilic design in school common areas—specifically corridors, stairwells, and central halls—on users’ perceptual and physiological responses. Biophilic design attributes were categorized into direct experiences (Plants & water) and indirect experiences (Materials & Images), and simulation stimuli for each common area type were generated using generative AI. Thirty university students participated in the experiment, where their hemodynamic responses (fNIRS) and galvanic skin responses (GSRs) were measured during exposure to various biophilic environmental stimuli to quantitatively analyze emotional arousal and cognitive recovery levels. The results indicated that biophilic environments elicited significant physiological stabilization responses in specific spatial and application conditions compared to non-biophilic settings. Distinct physiological responses were observed based on spatial characteristics and application methods; vertical elements facilitated cognitive rest, whereas horizontal elements promoted attention restoration through moderate arousal. Furthermore, significant associations between nature connectedness and selected physiological responses highlighted the importance of considering individual predispositions in spatial design. As an exploratory pilot study, this research contributes preliminary evidence by integrating generative AI-based simulations with fNIRS and GSR measurements to examine vertical and horizontal biophilic applications in school common areas. Full article

Monitoring boreal above-ground biomass (AGB) change requires approaches that are both measurement-based and spatially explicit. We integrated permanent sample plots from Quebec and Ontario with Landsat-7 spectral trajectories (1999–2023) to quantify non-fire-related AGB change after excluding wildfire-affected intervals and to evaluate whether annualized AGB change can be predicted from spectral change at the plot-interval scale. Tree height was estimated using a multilayer perceptron model (R² = 0.83) and combined with species-specific allometry to derive plot-level AGB and interval ΔAGB. These estimates were aggregated to ecodistricts using effective sample sizes and confidence intervals. Across well-sampled ecodistricts, mean annualized ΔAGB ranged from −0.82 to +3.54 t ha⁻¹ yr⁻¹, with lower or negative changes mainly occurring in eastern regions. Spectral indices derived from NIR–SWIR bands showed relatively stronger associations with ΔAGB than greenness-based indices, consistent with the sensitivity of moisture- and disturbance-related metrics to canopy stress, including defoliation. An XGBoost ensemble correctly predicted the direction of change in 77% of intervals. These results provide a measurement-constrained and scalable framework for monitoring non-fire-related biomass change and supporting greenhouse-gas reporting across boreal forest landscapes. Full article