Search Results (8,420)

Near-infrared spectroscopy (NIRS) offers significant advantages for the rapid and non-destructive detection of nutrients in livestock manure slurry. However, conventional models based only on spectral features often show limited robustness under cross-seasonal and multi-farm conditions due to differences in farm source, treatment stage, and complex spatiotemporal background. To improve the accuracy and applicability of total nitrogen (TN) prediction in dairy farm manure slurry, this study used 747 samples collected from 36 large-scale dairy farms in Tianjin, China, covering 24 treatment stages and four seasons, together with sample-contextual information such as farm name, longitude, latitude, and season. Competitive adaptive reweighted sampling (CARS) was applied to select key wavelengths from near-infrared spectra. On this basis, a multi-branch gated fusion deep learning model, MultiScaleSE-GatedCNN, was developed to integrate spectral and sample-contextual information. The model combines multi-scale one-dimensional convolution for spectral feature extraction, separate encoding branches for numerical and categorical inputs, and a gated fusion unit for adaptive weighting of different information sources. Results showed that partial least squares regression remained a strong baseline under single-source spectral conditions, but the proposed deep learning fusion model achieved superior predictive performance after introducing sample-contextual information. Ablation experiments demonstrated that different combinations of sample-contextual information contributed differently to model performance, and the combination of spectra, farm name, longitude, and season yielded the best results. Under this optimal input combination, MultiScaleSE-GatedCNN achieved a test-set R² of 0.905, an RMSEP of 367.389, and an RPD of 3.242. These results demonstrate that integrating NIRS with sample-contextual information can effectively improve the accuracy and robustness of TN prediction in dairy farm manure slurry. Full article

Background: Lumbar disc herniation (LDH) is the most common etiological cause of low back pain (LBP). Objective and precise pain evaluation is of significant clinical value. Functional near-infrared spectroscopy (fNIRS) as a noninvasive neuroimaging modality, has been increasingly validated to reflect subjective pain perception through hemodynamic correlates. This study aimed to analyze the fNIRS changes in patients with LDH about to receive Unilateral Biportal Endoscopy and to further explore the feasibility of fNIRS as an objective biomarkers for clinical assessment of LDH. Methods: Resting-state fNIRS data were acquired from 67 preoperative LDH patients and 20 healthy controls (HC). Brain functional maps—including z-standardized fractional amplitude of low-frequency fluctuations (zfALFF) and seed-based functional connectivity (FC)—were extracted and quantified. Group-level comparisons were performed between LDH and HC groups across four predefined regions of interest; additionally, correlation analyses were conducted between fNIRS metrics and clinical assessment scores within the LDH cohort. Results: Compared with HC, LDH patients exhibited significantly altered zfALFF in the medial prefrontal cortex (mPFC): decreased amplitude at channel CH12 (t = −2.031, p = 0.045) and increased amplitude at CH21 (t = 2.462, p = 0.016). Whole-brain FC analysis further revealed widespread changes—particularly between the parietal somatosensory cortex and prefrontal regions. Among all tested FC–clinical indicator associations, 56 reached statistical significance after FDR correction (q < 0.05). VAS_ lumbar and SF-36_SF exhibited the highest number of significant connections. Conclusions: LDH patients with LBP exhibit notable alterations in prefrontal resting-state ALFF and FC between the parietal somatosensory cortex and prefrontal cortex relative to HC. Importantly, these neural alterations exhibit significant associations with both pain severity (VAS) and long-term health-related quality of life (SF-36), thereby strengthening their candidacy as neural correlates meriting prospective validation as objective, mechanism-informed biomarkers for clinical evaluation of lumbar disc herniation (LDH). Moreover, these findings highlight candidate neural targets for future longitudinal studies investigating early prognostic prediction and treatment response monitoring in LDH. Full article

Pasture plants can contribute to ruminant nutrition and may, depending on composition, influence rumen fermentation and methane production. This study evaluated the nutritional composition, bioactive compounds, and methane production potential of 32 terrestrial plant species commonly foraged by goats in Malta. Dried plant samples were analysed for proximate composition using near-infrared spectroscopy, total polyphenols using the Folin–Ciocalteu assay, antioxidant activity using the DPPH assay, and methane production using an in vitro rumen fermentation system incubated for 48 h, with rumen fluid pooled from three goats (analyses performed in triplicate). Crude protein ranged from 1.16 to 31.97% DM, neutral detergent fibre from 12.29 to 48.89%, and ash from 9.69 to 17.20% across species. Total polyphenolic content varied from 0.07 to 1.30% (w/w), while antioxidant activity (IC₅₀) ranged from 0.37 to 55.9 mg/mL. Methane production after 48 h ranged from 30.39 to 198.26 L CH₄ kg⁻¹, indicating variation in fermentation characteristics among species. These results indicate that Rumex bucephalophorus and Urtica pilulifera demonstrated relatively high protein or bioactive values and comparatively lower in vitro methane-related parameters under the conditions tested. Full article

In this study, a low-cost AS7265x-based multispectral electronic tongue system was developed for estimating milk composition and adulteration indicators and supported with an explainable artificial intelligence (XAI) framework. Experimental analyses were conducted on 190 augmented commercial milk samples, where fat, protein, solids-not-fat (SNF), density, freezing point, and added water ratio were treated as target variables. Sensor data were modeled as RAW, DERIVED, and FUSION feature sets, and regression performance was compared using Random Forest, Gradient Boosting, AdaBoost, KNN, and XGBoost. Model validation was carried out with both five-fold cross-validation and Leave-One-Out (LOO) strategies to assess field-level generalizability. Results showed that a narrow-band, low-cost optical sensor platform can estimate not only fat and protein but also SNF, density, and freezing point with high accuracy. Within the XAI framework, permutation-based importance analysis and SHAP were used to identify critical spectral bands for each target parameter, enabling data-driven recommendations for band-oriented sensor design optimization. The study presents a scalable methodology that integrates low-cost sensor design, multi-parameter quality estimation, and explainable modeling beyond traditional fat–protein-focused approaches. Across all six targets, the XAI analysis consistently identified the near-infrared channel at 860 nm (asIR_3) as the most informative band, reflecting the combined effect of water absorption and Mie scattering by fat globules; the visible channel at 680 nm (asVIS_4) emerged as a secondary band, reflecting dissolved-matter scattering. These bands are therefore the natural starting point for cost-reduced versions of the sensor. Among the compared feature sets (RAW, DERIVED, FUSION), the 18-band RAW configuration provided the most balanced performance across all six targets. Full article

This study aims to enhance the early warning and monitoring of frost damage in triticale (×Triticosecale Wittmack), as well as to identify frost-tolerant materials. To this end, this work focused on phenotyping the dynamics of triticale under different damage intensities using spectral indices. Sixteen triticale genotypes were planted under three sowing date (SD) treatments, with three sowing rate (SR) gradients set for each SD. The multispectral data of triticale under six frost damage intensities were acquired using an unmanned aerial vehicle (UAV) platform. A total of eight spectral indices (SIs) were extracted from samples under each intensity. In general, for each combination of SD and SR, all SIs decreased monotonically with increasing damage intensity. These indices are therefore suitable for monitoring frost damage in triticale under complex sowing scenarios. Under early frost damage, the relative decline rates (RDRs) of the SRI (Simple Ratio Vegetation Index), EVI2 (Enhanced Vegetation Index 2), NIRv (Near-Infrared Reflectance of Vegetation), and GLI (Green Leaf Index) were higher than those of other indices, indicating that they are more sensitive to early frost damage and thus more suitable for frost warning. Under frost stress, the RDRs of the indices were higher in early-sown samples than in late-sown samples. SD plays a more significant role than SR in determining the response of triticale indices to frost damage. Models were developed to detect triticale under varying damage intensities with SIs and classification algorithms—XGBoost, Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM). The SVM classifier demonstrated the best generalization performance (overall accuracy: 98.03%; F1-score: 0.98). The detection contributions of indices within the optimal model were evaluated by their respective SHAP (Shapley Additive Explanations) values. The GLI, NIRv, NDVI (Normalized Difference Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index) were identified as key indices, as they exhibit higher cumulative SHAP values. Identification models for triticale with different frost tolerance levels were established based on the time-series data of these key indices and the above four algorithms. The optimal model based on the SVM algorithm achieved an identification accuracy exceeding 90%. The average overwintering dynamics and frost damage responses of the key indices were analyzed for triticale with different frost tolerance levels under all treatments. Under frost stress, these indices and their RDRs in frost-tolerant triticale were generally higher and lower, respectively, than those in frost-sensitive triticale. These four key indices can thus assist in the identification of frost tolerance in triticale. This study aids in the early warning and monitoring of frost damage in triticale under complex planting scenarios and the evaluation of overwintering performance in triticale germplasm. Full article

Smooth muscle (SM) dysfunction contributes to several pathological conditions, including atherosclerosis; current treatment strategies often fail to restore functional contractility. Adipose-derived stem cells (ADSCs) offer a promising cell source for regenerative medicine due to their accessibility and multipotency. Their differentiation into smooth muscle cells (SMC) is commonly driven by biochemical cues such as retinoic acid and transforming growth factor β; however, supporting this process with additional, non-invasive stimuli may enhance outcomes. Photobiomodulation (PBM) has emerged as a potential modulator of cellular metabolism, mitochondrial function and lineage commitment; however, its role in ADSCs to SMC differentiation remains insufficiently defined. ADSCs were irradiated with green (525 nm), near-infrared (825 nm) or dual wavelengths at 5 J/cm² and 10 J/cm² alongside the growth factors. Proliferation, cytotoxicity, mitochondrial membrane potential, collagen production, migration and smooth muscle marker expression were assessed. PBM induced a fluence-dependent biphasic response. 5 J/cm² fluences enhanced proliferation, mitochondrial activity, collagen deposition and organized SMC marker expression, whereas 10 J/cm² fluences lowered proliferation and membrane potential, reduced collagen and increased migration. PBM at 5 J/cm², especially greenlight, most effectively promoted ADSCs’ progression towards a SMC-like phenotype, with features consistent with a more contractile-like state. 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

This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R² > 0.70), while pH and texture fractions showed moderate performance (R² = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K₂O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients. Full article

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000−10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

Laparoscopic cholecystectomy is now established as the worldwide gold standard for the treatment of benign gallbladder disease. Despite technical advancements, bile duct injury (BDI) remains a major concern, especially in patients with obesity. It is well known that in patients with a Body Mass Index (BMI) ≥ 30 kg/m², identification of Calot’s triangle and the achievement of the Critical View of Safety (CVS) during laparoscopic cholecystectomy (LC) are made more challenging due to excessive visceral adiposity and concomitant hepatic steatosis reducing the workspace. Near-Infrared Fluorescence Cholangiography (NIRF-C) with Indocyanine Green (ICG) has emerged as an innovative, safe and effective technique to visualize the biliary anatomy and minimize the risk of iatrogenic BDI. However, its specific benefit in patients with obesity remains under-discussed compared to the general population. The main aim of this narrative review is to evaluate whether the intraoperative use of ICG in patients with obesity may reduce operative times and the risk of BDI. A focused review of the literature is performed on articles from 2010 to 2025 published on PubMed, Scopus and Web of Science. The application of ICG fluorescence in LC for patients with obesity represents a tangible clinical advantage, not only for anatomical identification and significant improvement of procedural efficiency, but also for the reduction in operative time. 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

Ultrafast fiber lasers operating near 2 μm have emerged as a critical platform for advancing mid-infrared photonics due to their narrow pulse durations, high peak powers, and broad tunability. These sources exploit the rich energy-level structures of Tm³⁺ and Ho³⁺ doped fibers and reside within an atmospheric transmission window, enabling applications spanning nonlinear microscopy, precision micromachining, optical frequency metrology, biophotonics, and free-space optical communication. Recent progress in low-loss fiber fabrication, dispersion-engineered cavity design, and mode-locking technologies has significantly expanded the performance boundaries of 2 μm ultrafast fiber lasers. This review systematically examines the underlying pulse-formation mechanisms and categorizes state-of-the-art mode-locking approaches. Representative laser architectures are compared with respect to pulse duration, energy scalability, repetition-rate enhancement, spectral characteristics, and environmental stability. Key application pathways in high-resolution spectroscopy, biomedical diagnostics, and mid-IR supercontinuum generation are highlighted. Finally, the remaining challenges and prospective research directions are discussed to inform the development of next-generation ultrafast photonic sources in the 2 μm band. Full article

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments. Full article

In optical sensing, electromagnetically induced transparency (EIT) and bound states in the continuum (BIC) substantially enhance light–matter interactions by leveraging high-Q resonances. This study theoretically demonstrates dual-resonance phenomena—namely, a quasi-symmetry-protected BIC (SP-BIC) and a quasi-BIC-induced EIT-like (QBIC-EIT) resonance—using a dielectric metasurface composed of pyramid-shaped lithium niobate nanoarrays operating in the near-infrared. The QBIC-EIT transmission window originates from the interference between surface lattice modes and toroidal dipole modes, triggered by symmetry breaking of the BIC state. Due to the absence of C_4v rotational symmetry in the pyramidal unit cells, the metasurface exhibits pronounced polarization-dependent responses: Under x-polarized incidence, a single quasi-SP-BIC resonance appears; under y-polarization, dual quasi-SP-BIC resonances along with a distinct QBIC-EIT resonance are observed. Both the high-Q quasi-SP-BIC resonance and the EIT-like window show strong sensitivity to changes in the ambient refractive index (RI). Specifically, the EIT-like window achieves a sensitivity of 404.9 nm/RIU, while the quasi-SP-BIC resonance delivers an exceptional sensitivity of 887.7 nm/RIU, confirming the metasurface’s performance as a high-sensitivity RI sensor. These findings establish a multi-band detection platform for advanced RI sensing and contribute to the development of high-performance metasurface-based optical sensors. Full article

Pancreatic lipase (PL) inhibition is a promising dietary strategy for obesity management. In this study, the inhibitory mechanisms and structural basis of polyphenols extracted from different sugarcane fractions were investigated using in vitro enzyme assays, spectroscopy, and molecular docking analyses. PL inhibitory activity was evaluated using p-nitrophenyl laurate (pNPL) as the substrate, with all assays performed in triplicate and results statistically analyzed. Among the extracts, sugarcane peel polyphenols (SP) exhibited the strongest inhibition, with a half-maximal inhibitory concentration (IC₅₀) of 31.56 mg/mL, significantly lower than that of sugarcane juice polyphenols (SJ, 55.86 mg/mL) and sugarcane bagasse polyphenols (SB, 65.31 mg/mL). Enzyme kinetic analyses revealed a reversible mixed-type inhibition mechanism. In contrast to crude extracts, individual phenolic monomers showed substantially lower IC₅₀ values (0.13–1.33 mg/mL), highlighting the intrinsic dilution. Compositional analysis identified ferulic acid, gallic acid, chlorogenic acid, and schaftoside as key contributors to PL inhibition. Fourier transform infrared (FTIR) and fluorescence spectroscopy demonstrated that polyphenols altered PL secondary structure by modulating α-helix and β-sheet contents and perturbed the microenvironment of tryptophan (Trp) and tyrosine (Tyr) residues. Molecular docking further indicated that these compounds bind within or near the substrate-binding channel via hydrogen bonding and hydrophobic interactions, engaging critical residues including Ser152, His263, and Phe77, and potentially influencing conformational elements involved in active-site accessibility. Collectively, these results suggest that sugarcane, particularly its peel, represents a valuable natural source of PL inhibitors. Despite the relatively high IC₅₀ values of crude extracts, their inhibitory activity arises from multicomponent contributions and supports their potential application as dietary modulators of fat digestion rather than as pharmaceutical lipase inhibitors. Full article