Search Results (366)

Efficient textile recycling depends on accurate identification of fibre types and compositions to support high-value material recovery and automated sorting. Existing commercial systems based on near-infrared (NIR) spectroscopy offer robust performance, but their model architectures and development methods are proprietary, and they often struggle to detect materials when carbon-black (graphite-based) dyes suppress the spectral signatures. This paper presents a hyperspectral imaging approach for textile fibre identification, combined with an artificial intelligence model capable of detecting cotton, polyester, elastane, and regions affected by carbon-black dye. Sixty-five textile samples were laboratory-verified to determine constituent materials and compositions, with 52 used in model development and testing. A semi-automatic algorithm detected textile boundaries and sampled 100 spectral patches per image. For materials exhibiting two distinct spectral signatures, typically due to carbon-black dye regions, 100 samples were collected for each signature, producing a database of 6500 spectra. A convolutional neural network model was trained using these signatures to predict fibre composition and identify any regions with carbon-black dye. The system achieved mean absolute errors below 2.1% for cotton, polyester, and elastane. A spatial clustering step groups pixels with similar spectra prior to detection, enabling region-wise material identification and allowing the model to classify clusters likely affected by carbon-black dye. This approach demonstrates high precision in fibre identification and reliable detection of carbon-black regions, highlighting its suitability for real-world textile analysis workflows. Full article

Muscle oxygen nation impairment, defined as a mismatch between oxygen delivery, distribution, and oxidative utilization in active skeletal muscle, contributes to exercise intolerance and functional decline. Near-infrared spectroscopy (NIRS) has emerged as the leading non-invasive tool for monitoring local muscle oxygenation, but its clinical translation and optimal exercise-based management remain incompletely defined. This scoping review aimed to (1) synthesize the pathophysiology of muscle oxygenation impairment across the oxygen transport cascade, (2) evaluate NIRS-based diagnostic protocols, and (3) review exercise-based interventions targeting muscle oxygenation. The review followed PRISMA-ScR guidelines and was prospectively registered in OSF (DOI: 10.17605/OSF.IO/QW8R3) and PROSPERO (CRD420261365040). PubMed, Web of Science, Scopus, Cochrane CENTRAL, EMBASE, PEDro, and ClinicalTrials.gov were searched through to April 2026. Methodological quality was appraised using the PEDro scale, the Downs and Black checklist, and the Newcastle–Ottawa Scale. A total of 61 studies (2003–2025) were included, with fair-to-good methodological quality (PEDro 3–8, mean 5.3; Downs and Black 15–24, mean 18.6; Newcastle–Ottawa 5–8, mean 6.5). Regarding pathophysiology, muscle oxygenation impairment is a cascade-level phenomenon with four mechanistically distinct phenotypes corresponding to the dominant site of impairment, each with characteristic NIRS signatures. Regarding diagnostic assessment, NIRS has shown value in selected contexts including a validated threshold for peripheral artery disease, but most studies report group-level correlations without deriving receiver operating characteristic curves at validated thresholds, which together with device and calibration heterogeneity limits clinical translation. Regarding exercise-based interventions, adaptations align with the underlying cascade lesion, sprint and high-intensity interval training enhance oxidative capacity, while walking-based and vascular-targeted programs preferentially improve microvascular function. These findings support a unifying framework in which the site of cascade impairment guides diagnostic protocol selection and exercise prescription. The proposed cascade lesion phenotyping schema is hypothesis-generating and requires prospective validation. Full article

Infrared radiation spectra produced by vibration–rotation transitions in multicomponent gases within the vacuum plume of attitude and orbital control engines constitute crucial radiation sources for optical target identification and space maneuver recognition, and rapid prediction of these signatures is essential for real-time forecasting. This study introduces an axisymmetric vacuum plume flow field model based on a simplified point-source approach that accommodates multicomponent combustion gases. Using the Maxwellian velocity distribution and a velocity–position angle algorithm, normalized number density, velocity, and temperature distributions are derived. A plume–freestream interaction model founded on noncentral fully elastic collision theory is incorporated, and overall plume properties are obtained via density-weighted averaging. Neglecting non-equilibrium radiation effects, the high-temperature gas absorption coefficient is calculated using a statistical narrowband model and radiative transfer is solved via the line-of-sight method. The model is validated against direct simulation Monte Carlo results for single-gas and MBB bipropellant plumes and confirmed using infrared spectral data in the 2.0–4.5 μm band. The proposed framework achieves 10²–10³-fold higher computational efficiency than conventional DSMC approaches. Freestream effects on plume diffusion and momentum exchange diminish with increasing altitude, as does the freestream velocity’s enhancement of radiation intensity, whereas greater plume expansion at higher altitudes increases overall radiation intensity. Full article

Industrial control of volatile organic compound (VOC) emissions from medium-density fibreboard (MDF) production remains constrained by a shortage of compound-resolved evidence from full-scale plants, where wood furnish, amino resin chemistry, heat transfer, gas flow, and wet gas cleaning act simultaneously. Here, we analysed more than 20,000 synchronized operating records from a full-scale single-stage flash-tube MDF dryer at an industrial SWISS KRONO production line and linked total VOC (TVOC) measurements from flame ionization detection with Fourier-transform infrared speciation on the cleaned stack. Five compounds—α-pinene, 3-carene, limonene, methanol, and formaldehyde—accounted for more than 80% of the resolved VOC signal. Process–state contrasts showed that higher digester residence time, discharge screw speed, adhesive amount, urea amount, dryer inlet temperature, and scrubber–water temperature increased one or more representative compounds, whereas higher hardwood share, additional flue-gas supply, and higher scrubber–water pH decreased them. Limonene, methanol, and formaldehyde were substantially more process-sensitive than α-pinene. An exploratory decorrelation step further showed that a drying/throughput domain explained about half of the variability of the screened process space. The study therefore identifies the small set of compounds and operating domains that most strongly govern the cleaned dryer-stack signature and provides a mechanistically grounded prioritization framework for follow-up causal experiments, source apportionment, and emission-mitigation design in industrial MDF manufacture. Unlike product or chamber emission studies, this work links the compound-resolved FTIR/FID chemistry of the final cleaned industrial stack with synchronized production variables; it therefore addresses a scale-integration gap by transforming routine compliance-type exhaust monitoring into a process-diagnostic framework for ranking emission sources, abatement-sensitive variables, and mitigation experiments. Full article

Nanoplatforms combining multiple imaging contrast modalities are gaining interest across life sciences and beyond. Here, we disclose a proof-of-concept series of harmonic nanoparticles (HNPs) conjugated with a variety of lanthanide (Ln) complexes, enabling tunable imaging properties. Building on our previous approach for the conjugation of Gd(III) complexes at the surface of HNPs through copper-catalyzed click chemistry, we first establish a copper-free alternative by benchmarking the signals of the resulting conjugates in magnetic resonance imaging phantoms. We then extend this system to Eu, Tb and Yb conjugates and investigate their photophysical properties, successfully detecting long-lived Ln emissions spanning the visible and near-infrared spectrum. Interestingly, the Ln ion can be efficiently removed and exchanged, allowing reuse of the same HNP with a new optical signature. Most notably, we demonstrate that the Eu luminescence can be indirectly activated via second-harmonic generation from the HNP core upon femtosecond-pulsed irradiation in parallel to direct two-photon excitation. This nonlinear activation scheme paves the way for the preparation of mixtures with multidimensional optical signatures using a single excitation source. Altogether this work provides a versatile framework to further explore HNP-Ln conjugates as multimodal imaging probes. Full article

Background/Objectives: Mental fatigue during driving can arise under different task conditions and typically progresses from mild to severe states. Active fatigue is usually linked to cognitively demanding driving, whereas passive fatigue is associated with prolonged monotonous driving. However, studies on multilevel mental fatigue remain scarce, and direct comparisons of prefrontal multimodal physiological responses to active and passive fatigue are still limited. The objective of this study is to characterize and compare the prefrontal multimodal physiological signatures across three fatigue levels under two simulated driving paradigms designed to induce active and passive fatigue. Methods: Eleven healthy participants completed two simulated driving tasks designed to induce active and passive fatigue. Physiological data were recorded using a self-developed prefrontal EEG-fNIRS system, and pulse-related signals were derived from the hemodynamic measurements. Based on subjective and objective indicators, fatigue was classified into non-fatigue (NonF), moderate fatigue (ModF), and severe fatigue (SevF). Results: In the active-fatigue-inducing paradigm, significant changes in prefrontal EEG and hemodynamic already emerged from NonF to ModF; for example, the EEG β/(θ + α) power ratio increased from 0.973 to 1.157 (p < 0.001) and the normalized mean deoxyhemoglobin feature increased from −0.06 to 0.09 (p < 0.001). In the passive-fatigue-inducing paradigm, EEG changes became prominent mainly from ModF to SevF, with β/(θ + α) power ratio decreasing from 0.806 to 0.761 (p < 0.05). Pulse rate variability showed increasing trends in both paradigms. Conclusions: These findings suggest that the two simulated driving paradigms were associated with distinct prefrontal electrophysiological, hemodynamic, and autonomic evolution patterns across three fatigue levels, supporting graded fatigue assessment and multimodal fatigue monitoring in driving. Full article

Chagas disease remains a major neglected parasitic illness in Latin America and other endemic regions, and benznidazole (BZN) is still the primary trypanosomacidal drug despite its incompletely understood mechanism of action. This work provides a detailed biophysical characterization of the conformational behavior and vibrational properties of benznidazole (BZN), a first-line trypanocidal drug still widely used for the treatment of Chagas disease. Using density functional theory combined with relaxed potential energy surface scans in vacuum and implicit water, two low-energy conformers (BZN1 and BZN2) were identified, separated by moderate rotational barriers and a small energy difference, indicating that both are intrinsically accessible at room temperature. For each conformer, infrared and Raman spectra were calculated and assigned via vibrational mode analysis, then compared with FT-IR and FT-Raman spectra recorded for pharmaceutical-grade polycrystalline BZN. The theoretical and experimental spectra show excellent agreement, with a Raman band in the 1350–1400 $\mathrm{c}{\mathrm{m}}^{-1}$ region emerging as a sensitive conformational marker: the experimental maximum at $1359\mathrm{c}{\mathrm{m}}^{-1}$ matches the most intense BZN1 mode, whereas the corresponding BZN2 band appears about $13\mathrm{c}{\mathrm{m}}^{-1}$ higher in frequency. This clear spectroscopic fingerprint demonstrates that the solid drug is overwhelmingly composed of the BZN1 conformer, despite the theoretical accessibility of BZN2. Overall, the study links the conformational landscape of benznidazole to its vibrational signatures and highlights Raman spectroscopy, supported by quantum chemical calculations, as a powerful tool for conformational and potential polymorphic control of this clinically important nitroimidazole. Full article

Background/Objectives: Early transient endocrine dysfunction after simultaneous pancreas-kidney transplantation (SPK) frequently triggers urgent investigations to exclude thrombosis, pancreatitis, or rejection, yet many recipients recover during the index admission. We tested whether pre-transplant day zero (D0) serum Fourier-transform infrared spectroscopy (FTIRS) captures a biochemical fingerprint associated with a Start&Stop trajectory (initial insulin independence followed by transient dysfunction with recovery). Methods: In a single-center retrospective case-control study nested within 104 consecutive SPK recipients with available D0 serum, 12 Start&Stop cases were matched 1:1 to 12 No-Stop controls. Serum FTIR spectra went through structured quality control and standardized preprocessing. A Naïve Bayes classifier with Fast Correlation-Based Filter (FCBF) feature selection was evaluated using leave-one-out cross-validation (LOOCV) and label-permutation analysis. Results: Under LOOCV, the primary FTIRS model (Savitzky-Golay second derivative; 600–900 and 2800–3400 cm⁻¹) achieved excellent discrimination (ROC-AUC 1.00) with accuracy 0.958 and F1 score 0.958. Discrimination collapsed under label permutation (ROC-AUC 0.461), supporting a non-random label-spectrum association. Discriminant information mapped mainly to carbohydrate/glycoprotein-associated bands (~946–1161 cm⁻¹), protein structural contributions near the amide III region (~1300 cm⁻¹), and lipid/protein stretching modes (~2865–3163 cm⁻¹), consistent with a multicomponent systemic biochemical state. Conclusions: In this exploratory matched case-control cohort, pre-transplant D0 serum FTIRS signatures were associated with the subsequent Start&Stop phenotype after SPK. These findings should be interpreted as recipient-side exploratory risk-stratification signals rather than clinically actionable decision tools. Larger multicenter validation in unselected cohorts, with standardized endpoint adjudication, preanalytical control, fully nested model development and inter-instrument harmonization, is required before clinical implementation or population-level risk calibration. Full article

Weed interference during early growth stages is a major constraint on sugarcane productivity, yet effective tools for species-specific detection remain limited in tropical agricultural systems. This study evaluated the spectral separability between Sugarcane (Saccharum officinarum) and a dominant weed species, Rottboellia cochinchinensis, to develop an accessible framework for early-stage weed mapping. Multispectral data acquired from an Unmanned Aerial Vehicle (UAV) and hyperspectral data obtained from a field spectrometer were utilized. Hyperspectral data were synthesized to reconstruct multispectral bands (UAV image bands) using a regularized linear synthesis model, thereby generating spectral signatures. Spectral separability between sugarcane and Rottboellia cochinchinensis was assessed visually and statistically (Jeffries–Matusita distance). Blue and Green bands provided the strongest differentiation between species, while RedEdge enhanced separability when paired with pigment-sensitive wavelengths. When using vegetation indices based on the near-infrared (NIR) band, the visual appearance of class separation was poor due to the NIR band’s sensitivity to variation in leaf internal structure, canopy architecture, water content, and spectral mixing with the soil background at the early stage of sugarcane. These results were used to differentiate weed coverage from sugarcane. Object-based image analysis (OBIA) outperformed the pixel-based method, achieving higher overall accuracy (0.9038) and a more spatially coherent weed delineation (Kappa = 0.8499). These findings suggest that synthesized spectral signatures of Rottboellia cochinchinensis and sugarcane, combined with targeted spectral indices and OBIA techniques, offer a practical and transferable approach for early detection of Rottboellia cochinchinensis at the farm level. Full article

Remote sensing is widely used in archaeological prospection to detect surface anomalies (crop marks) indicating buried remains, typically through recognition of visual patterns in high- or very high-resolution imagery acquired by means of satellite, airborne, or drone sensors. In contrast, spectroscopic approaches focusing on variations in spectral signatures still remain rarely applied in archaeological research. This study proposes a technological barrier-free method addressed to archaeologists which is based on pixel-level analysis of the Reflectance Values (RV) and spectral shape variations in the visible, near-infrared and short-wave infrared (VIS-NIR-SWIR) range derived from Sentinel-2 imagery. Spectral signatures are extracted through sampling polygons designed to account for the spatial resolution of the different Sentinel-2 bands and their spatial relationship with the location and size of the archaeological features. The RV method is tested on two Roman archaeological contexts: the ancient city of Telesia Vetere (San Salvatore Telesino, Benevento) and a Roman villa at Podere Colle Agnano (Labro, Rieti) using the full Sentinel-2 archive since 2017. While Telesia has previously been investigated through aerial photo interpretation and archaeological fieldwork, the Roman villa at Labro is documented here for the first time. Results show consistent seasonal repeated spectral separability between areas corresponding to known buried archaeological features and surrounding areas. Similar anomalies were also detected in areas without previously documented remains, thus suggesting the possible presence of buried structures and highlighting the predictive potential of the RV method. Owing to its easiness to use beyond image processing specialism and reliance on open-access data, the method can support archaeological decision-making and guide further investigation with higher-resolution remote sensing data or targeted field surveys, particularly in the framework of preventive archaeology. Full article

Thermal infrared remote sensing offers a cost-effective means of regional geothermal reconnaissance, yet a fundamental challenge remains: isolating the weak geothermal surface signal (typically 1–3 °C) from dominant surface noise introduced by seasonal temperature cycles (annual amplitude > 20 °C), topographic variability, land cover heterogeneity, and irregular cloud-affected satellite sampling. Conventional single-scene or arithmetic-mean approaches are highly susceptible to these confounding factors and frequently produce pseudo-anomalies that obscure genuine geothermal targets. To overcome this limitation, we propose a physics-based time-series framework in which a nonlinear annual temperature variation model, T(t) = T0 + A·sin(2πt/τ + φ), is fitted to multi-temporal Landsat 8 thermal infrared data via the Levenberg–Marquardt algorithm. Applied to ~50 cloud-free scenes (2021–2022) processed on the Google Earth Engine over the Shanxi Graben System, northern China, the model simultaneously retrieves the background temperature parameter T0 and seasonal amplitude A—two physically interpretable quantities that encode distinct geothermal signatures more robustly than simple temporal statistics. Sub-regional corrections for the elevation (−4 °C/100 m above 800 m), aspect (R² > 0.95 in piecewise linear segments), and slope further suppress topographic pseudo-anomalies prior to anomaly extraction. Over known high-temperature geothermal fields (Tianzhen and Yanggao; >100 °C at 100 m depth), the method reveals clear T0 offsets of +1–2 °C (3–5% relative) and amplitude deficits of ~2 K (5–10% relative) relative to the background, with model-fitted T0 values averaging ~2 °C higher than arithmetic means due to the correction for seasonal sampling bias. Combined with 5 km fault-proximity buffers, extracted anomaly zones align well spatially with known geothermal sites and major structural corridors of the graben system. However, deeper low-temperature systems (45–50 °C at 300–500 m depth) produce ambiguous signals below the ~1.5 K detection threshold, indicating inherent limitations for deeply buried resources. The fully reproducible, training-data-free workflow is implementable via open satellite archives and cloud computing platforms, making it a transferable low-cost tool for structurally controlled geothermal reconnaissance across extensional basins worldwide. Full article

Reliable detection of personnel is critical for situational awareness and life-saving interventions during indoor fire rescue operations, where dense smoke rapidly obscures visibility and compromises conventional vision systems. Visible-light cameras fail under such conditions due to severe Mie scattering, while thermal infrared (TIR) imaging—though capable of penetrating smoke—often lacks the fine-grained texture needed to distinguish human forms from background clutter. Furthermore, practical deployment of multimodal sensors is hindered by spatial misalignment between modalities, which degrades fusion efficacy and detection accuracy. To address these challenges, this paper proposes GLAFC-YOLO (Global-Local Alignment and Frequency-aware Cross-attention Fusion), a dual-stream multimodal detection framework specifically designed for personnel localization in smoke-obscured indoor fires. GLAFC-YOLO fuses near-infrared (NIR) and TIR imagery through three novel components: (1) a Global-Local Feature Alignment Subnet (GL-FAS) that rectifies geometric misalignment across modalities; (2) a Modality-Adaptive Frequency Channel Attention (MA-FCA) module that enhances complementary smoke-penetrating thermal signatures and NIR texture cues in the frequency domain; and (3) a Confidence-Aware Transposed Cross-Attention (CA-TCA) mechanism that suppresses smoke-induced artifacts and restores weakened human-centric features. Evaluated on a newly collected multimodal dataset of indoor fire scenarios with annotated personnel, GLAFC-YOLO achieves substantial improvements over the baseline YOLOv11 architecture. Specifically, it achieves Recall improvements of 43.2% and 0.5% compared to unimodal NIR and TIR baselines, respectively. In addition, it achieves improvements of 37.4% and 3.9% in $mA{P}_{50}$ and 17.3% and 17.0% in $mA{P}_{50–95}$. Experimental results indicate that GLAFC-YOLO outperforms competitive models and reduces personnel miss rates, demonstrating its robustness and readiness for real-world fire-rescue assistance. Full article

Native cellulose I can be converted into crystalline polymorphs II and III_I, while cellulose II can be further converted into III_II through chemical treatments that induce significant structural, physical, and chemical changes. Accurate identification and differentiation of these polymorphs is essential for predicting fiber reactivity and processing behavior, but current methods are time-consuming. This study demonstrates the potential of near-infrared hyperspectral imaging (HSI-NIR) combined with linear discriminant analysis as a rapid, non-destructive tool for polymorph discrimination. Cellulose I, II, III_I, and III_II were produced from bleached kraft pulps of eucalyptus and pine and from cotton linters using NaOH (20% w/v) and ethylenediamine treatments. HSI-NIR successfully differentiated polymorphs based on spectral signatures in the 1480–1600 nm range, regardless of botanical source. Complementary characterization by XRD confirmed polymorph conversions, showing crystallinity reductions of 10–15% for cellulose I→II and I→III_I conversions, with crystallite size decreasing from 7.2 nm (cotton CI) to 3.2–3.4 nm in all CIII_II samples. XPS analysis revealed increased C-O surface accessibility in cellulose II and III, with complete disappearance of COOH groups in cellulose III samples. These results establish HSI as a promising screening tool for cellulose polymorph identification (>95% classification accuracy) and provide comprehensive baseline data on structural and chemical transformations that govern fiber reactivity in chemical and enzymatic processes. Full article

This study evaluated the performance of Fourier-transform infrared (FTIR) spectroscopy for identifying spectral signatures associated with two key traits of Listeria monocytogenes: serogroup classification and biofilm-forming capacity. A total of 100 strains, previously serogrouped by PCR and categorized as high, intermediate, or low biofilm producers, were analyzed. Whole-genome sequencing was performed, and comparative genomics was conducted at core-genome, pangenome, and whole-genome (k-mer) levels to determine which genomic representation best reflected the phenotypes. Strains were typed using Fourier-Transform Infrared (FTIR Biotyper^® system from Bruker Daltonics GmbH and Co., Bremen, Germany) with five technical replicates. Spectral data from the polysaccharide region (1300–800 cm⁻¹) were extracted and used to train twelve statistical models within a machine learning pipeline combined with cross-validation to predict four serogroups and three biofilm clusters from 501 spectral variables. Genomic analyses showed strong concordance between population structure and serogroup, whereas biofilm formation displayed only weak genomic association, explaining less than 0.1% of genomic variance (PERMANOVA R² ≤ 0.001). Penalized discriminant analysis achieved the highest performance for serogroup prediction (overall accuracy 97.2%), while the k-nearest neighbor model performed best for biofilm prediction (74.8%). Two dedicated R Shiny applications were developed to facilitate model use. Overall, FTIR spectroscopy coupled with machine learning can provide a rapid and cost-effective alternative to PCR, genomic analyses, and in vitro assays for phenotypic trait prediction. Full article

The construction of Dyson spheres, megastructures designed to capture the total radiative output of stars, can be one of the most compelling techno-signature scenarios for advanced extraterrestrial civilizations. By considering equilibrium temperatures, we investigate the luminosities and fluxes of Dyson spheres built around two promising classes of host stars: white dwarfs and red M-dwarfs. Using radiative balance arguments and representative stellar parameters, we compute the temperature–radius relationship for full energy interception and place these hypothetical structures on the Hertzsprung–Russell (H–R) diagram to assess their observational signatures. Our results show that Dyson spheres around white dwarfs produce cooler and fainter blackbody emissions, peaking in the near- to mid-infrared, while those around M-dwarfs radiate more strongly but at longer wavelengths. In both cases, the equilibrium temperature decreases as $R_{\mathrm{D}}^{-1/2}$, while the total luminosity and observed bolometric flux remain fixed by the stellar output. These findings highlight the astrophysical suitability of low-luminosity stars as Dyson sphere hosts and provide practical constraints for future techno-signature searches using infrared surveys. Full article