Search Results (2,175)

This study explored the feasibility of noninvasive blood glucose (BG) estimation using near-infrared (NIR) spectroscopy with dog blood samples. A sensor module employing three representative wavelengths (770 nm, 850 nm, and 970 nm) was tested on an artificial blood vessel (ABV) and a thin pig skin (TPS) model. BG concentrations were adjusted through dilution and enrichment with injection-grade water and glucose solution, and reference values were obtained from three commercial invasive glucometers. Correlations between NIR spectral responses and glucose variations were quantitatively evaluated using linear, multiple, partial least squares (PLS), logistic regression, regularized linear models, and multilayer perceptron (MLP) analysis. The results revealed distinct negative correlations at 850 nm and 970 nm, identifying these wavelengths as promising candidates for noninvasive glucose sensing. Furthermore, an NIR–glucose database generated from actual dog blood was established, which may serve as a valuable resource for the development of future noninvasive glucose monitoring systems. Full article

Leaf water content (LWC) and chlorophyll content (CHL) are pivotal physiological indicators for assessing citrus growth and stress responses. However, conventional measurement techniques—such as fresh-to-dry weight ratio and spectrophotometry—are destructive, time-consuming, and limited in spatial and temporal resolution, making them unsuitable for large-scale monitoring. To achieve efficient large-scale monitoring, this study proposes a synergistic inversion framework integrating UAV multispectral remote sensing with intelligent optimization algorithms. Field experiments during the 2024 growing season (April–October) in western Hubei collected 263 ground measurements paired with multispectral images. Sensitive spectral bands and vegetation indices for LWC and CHL were identified through Pearson correlation analysis. Five modeling approaches—Partial Least Squares Regression (PLS); Extreme Learning Machine (ELM); and ELM optimized by Particle Swarm Optimization (PSO-ELM), Artificial Hummingbird Algorithm (AHA-ELM), and Grey Wolf Optimizer (GWO-ELM)—were evaluated. Results demonstrated that (1) VI-based models outperformed raw spectral band models; (2) the PSO-ELM synergistic inversion model using sensitive VIs achieved optimal accuracy (validation R²: 0.790 for LWC, 0.672 for CHL), surpassing PLS by 15.16% (LWC) and 53.78% (CHL), and standard ELM by 20.80% (LWC) and 25.84% (CHL), respectively; and (3) AHA-ELM and GWO-ELM also showed significant enhancements. This research provides a robust technical foundation for precision management of citrus orchards in drought-prone regions. Full article

The accelerating demand for climate action has underscored the need to link financial innovation with clean energy adoption. This study examines the interplay between green finance, renewable energy consumption, and CO₂ emissions across 15 countries from 2013 to 2022. Green finance is proxied by green bond issuances and environmental protection expenditures, capturing both market-based and fiscal flows. Using panel econometric methods, including fixed effects with Driscoll–Kraay corrections, Prais–Winsten regressions with PCSE, and Feasible Generalized Least Squares (FGLS), the analysis accounts for heteroscedasticity, autocorrelation, and cross-sectional dependence. Results show how green finance significantly reduces emissions, both directly and indirectly, through its positive influence on renewable energy deployment. Renewable energy consumption shows a robust negative association with CO₂ emissions, confirming its pivotal role in energy transition. A mediation analysis further demonstrates that renewable energy partially transmits the effect of green finance on environmental performance. The findings highlight the dual function of green finance in mobilizing investment and accelerating decarbonization, offering timely insights for policymakers seeking effective pathways toward sustainable, low-carbon economies. Full article

The proliferation of tunnel and subway networks in urban areas has heightened concerns regarding their vulnerability to seismic-induced liquefaction. This phenomenon, wherein saturated sandy soils lose strength and behave like a liquid under seismic waves, poses a catastrophic threat to the structural integrity and stability of underground constructions. While extensive research has been conducted to evaluate liquefaction triggering, most existing approaches rely on single ground motion intensity measures (e.g., PGA, I_A), which often fail to capture the combined effects of amplitude, energy, and duration on liquefaction behavior. In this study, the seismic response of saturated sandy soil from Yinchuan was analyzed using the Dafalias–Manzari constitutive model implemented in the OpenSeesPy platform. The model parameters were carefully calibrated using laboratory triaxial results. A total of ten real earthquake records were applied to evaluate two critical engineering demand parameters (EDPs): surface lateral displacement (SLD) and the maximum thickness of the liquefied layer (MTL). The results show that both SLD and MTL exhibit weak correlations with conventional intensity parameters, suggesting limited predictive value for engineering design. However, by applying Partial Least Squares (PLS) regression to combine multiple intensity measures, the prediction accuracy for SLD was significantly improved, with the correlation coefficient increasing to 0.81. In contrast, MTL remained poorly predicted due to its strong dependence on intrinsic soil characteristics such as permeability and fines content. These findings highlight the importance of integrating both seismic loading features and geotechnical soil properties in performance-based liquefaction hazard evaluation. Full article

Extra virgin olive oil (EVOO) can degrade during production or storage to virgin olive oil (VOO) or lampante olive oil (LOO). Fraud can also occur during commercialisation through the adulteration of EVOO (Ad-EVOO) with cheaper sunflower oil (SFO). Therefore, rapid screening techniques for quality control are needed. We evaluated an electronic nose (EN) with chemometrics—linear discriminant analysis (LDA), artificial neural-network discriminant analysis (ANN-DA), and partial least-squares regression (PLS)—in two scenarios: (i) classification into four classes (EVOO, VOO, LOO, and Ad-EVOO adulterated with 25% w/w SFO); and (ii) Ad-EVOO series containing 5–40% w/w SFO. Classes were corroborated by HS-SPME-GC-MS, with elevated (E)-2-hexenal and 3-hexen-1-ol in EVOO and increases in nonanal, ethyl acetate, and 2-propanol in deteriorated oils. Using the EN, LDA separated the classes, and ANN-DA achieved 90% accuracy under cross-validation, with the greatest confusion between VOO and LOO. In adulteration, discrimination emerged from 20% SFO, and PLS estimated %Ad-EVOO with R²pred = 0.972 (RMSEC/RMSEP = 8.059/5.627). In conclusion, the EN provides objective, rapid, and non-destructive screening that supports sensory panels and chromatographic analyses during reception and storage in industrial settings. Full article

Earthquakes are the result of complex interactions between tectonic plates, the mantle, and the lithosphere. Complex geodynamic conditions contribute to the occurrence of seismic phenomena. Tectonic plates can collide, move apart, or slide past each other. Mantle convection by internal heat drives plate motions that deform the lithosphere. Rocks deform elastically as stress accumulates and pore fluid pressure changes. Rupture occurs when stress exceeds frictional resistance. The connection between variations in gravity and the magnitude of earthquakes remains unclear. This work aims to examine aspects of this correlation. Three sets of earthquakes, one with events from all over the world, one from broader Greece, and one from the Hellenic Trench in Greece, aiming to cover all cases of geodynamics, from very different to very similar, were employed. Time series of gravity measurements at earthquake epicenters were extracted from GRACE satellite data. Time derivatives of the gravity field, as well as magnitude-dependent variations—reflecting changes relative to earthquake strength—were computed. Multiple linear regression (MLR), partial least squares (PLS) regression, and neural networks (NN) were used to model the relationship between gravity or its derivatives and earthquake magnitude. A correlation between the earthquake magnitude and magnitude derivatives was found. By using the global and Greek datasets, the best accuracy was obtained with MLR, reporting a mean squared error (MSE) of 0.069 with an R² of 0.979, and MSE was 0.011 with R² score of 0.997, respectively. By using the Hellenic Trench set, PLS regression derived the best correlation results, reporting an MSE of 0.004 and an R² of 0.977. Experimental results suggest that gravity, and therefore crustal density, is related to the magnitude of the impending earthquake, but not to its timing. Full article

In response to the growing safety challenges posed by large-scale and specialized freight transportation on China’s rapidly expanding highway network, this study investigates the operational characteristics of trucks in interchange diverging areas—a critical segment with elevated accident risks. Leveraging high-frequency trajectory data collected from 16 interchanges, we analyze speed profiles and acceleration behavior of heavy trucks across key sections: the diversion influence zone, preparation zone, transition segment, and deceleration lane. A key contribution of this work is the development of a continuous speed prediction model based on Partial Least Squares Regression, which integrates road geometric parameters and driving behavior features to estimate speeds at four critical cross-sections of the diverging process. Furthermore, we propose a comprehensive safety evaluation framework incorporating three novel indicators: longitudinal speed consistency, lateral stability, and deceleration comfort. The model demonstrates strong performance, with all mean absolute percentage errors below 10% during validation using data from four independent interchanges. Comparative analysis with existing safety standards confirms the practical applicability and accuracy of the proposed methodology. This research offers three major contributions: (1) a systematic approach for processing large-scale trajectory data and predicting truck speeds in diverging areas; (2) a safety assessment framework tailored for geometric design consistency evaluation; and (3) empirical support for optimizing traffic safety facilities in interchange design and operation. The findings address a significant gap in current highway design guidelines and provide actionable insights for enhancing safety in truck-dominated transportation environments. Full article

Ensuring the authenticity of meat products is a critical issue for consumer protection, regulatory compliance, and the integrity of local food systems. In this study, attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometric and machine learning models was applied to differentiate Maltese from non-Maltese pork. Spectral datasets were subjected to a range of preprocessing techniques, including Savitzky–Golay first and second derivatives, detrending, orthogonal signal correction (OSC), and standard normal variate (SNV). Linear methods such as principal component analysis–linear discriminant analysis (PCA-LDA), the soft independent modeling of class analogy (SIMCA), and partial least squares regression (PLSR) were compared against nonlinear approaches, namely support vector machine regression (SVMR) and artificial neural networks (ANNs). The results revealed that derivative preprocessing consistently enhanced spectral resolution and model robustness, with the fingerprint region (1800–600 cm⁻¹) yielding the highest discriminative power. While PCA-LDA, SIMCA, and PLSR achieved high accuracy, SVMR and ANN models provided a superior predictive performance, with accuracies exceeding 0.99 and lower misclassification rates under external validation. These findings highlight the potential of FTIR spectroscopy combined with nonlinear chemometrics as a rapid, non-destructive, and cost-effective strategy for meat authentication, supporting both consumer safety and sustainable food supply chains. Full article

This study develops an innovative unmanned aerial vehicle (UAV)-based intelligent system for winter wheat yield prediction, addressing the inefficiencies of traditional manual counting methods (with approximately 15% error rate) and enabling quantitative analysis of water–fertilizer interactions. By integrating an enhanced Faster Region-Based Convolutional Neural Network (Faster R-CNN) architecture with multi-source data fusion and machine learning, the system significantly improves both spike detection accuracy and yield forecasting performance. Field experiments during the 2022–2023 growing season captured high-resolution multispectral imagery for varied irrigation regimes and fertilization treatments. The optimized detection model incorporates ResNet-50 as the backbone feature extraction network, with residual connections and channel attention mechanisms, achieving a mean average precision (mAP) of 91.2% (calculated at IoU threshold 0.5) and 88.72% recall while reducing computational complexity. The model outperformed YOLOv8 by a statistically significant 2.1% margin (p < 0.05). Using model-generated spike counts as input, the random forest (RF) model regressor demonstrated superior yield prediction performance (R² = 0.82, RMSE = 324.42 kg·ha⁻¹), exceeding the Partial Least Squares Regression (PLSR) (R² +46%, RMSE-44.3%), Least Squares Support Vector Machine (LSSVM) (R² + 32.3%, RMSE-32.4%), Support Vector Regression (SVR) (R² + 30.2%, RMSE-29.6%), and Backpropagation (BP) Neural Network (R²+22.4%, RMSE-24.4%) models. Analysis of different water–fertilizer treatments revealed that while organic fertilizer under full irrigation (750 m³ ha⁻¹) conditions achieved maximum yield benefit (13,679.26 CNY·ha⁻¹), it showed relatively low water productivity (WP = 7.43 kg·m⁻³). Conversely, under deficit irrigation (450 m³ ha⁻¹) conditions, the 3:7 organic/inorganic fertilizer treatment achieved optimal WP (11.65 kg m⁻³) and WUE (20.16 kg∙ha⁻¹∙mm⁻¹) while increasing yield benefit by 25.46% compared to organic fertilizer alone. This research establishes an integrated technical framework for high-throughput spike monitoring and yield estimation, providing actionable insights for synergistic water–fertilizer management strategies in sustainable precision agriculture. Full article

The nutritional value of beef is highly influenced by its fatty acid composition. This study evaluated whether diet proximate analyses or plasma fatty acid profiles could predict the meat fatty acid composition in young beef cattle finished at pasture or with hay- and concentrate-based diets in stalls. Eighteen crossbred animals (Limousine × Sardo-Bruna) were analyzed for plasma and the intramuscular fat composition of Longissimus thoracis (LT) and Musculus gluteus maximus (MGM). A canonical correlation analysis revealed strong relationships between the dietary antioxidant capacity and meat lipid profiles, particularly for α-linolenic acid and conjugated linoleic acid. The redundancy index indicated that diet explained 38% of the variance in LT fatty acids and 20% in MGM. Partial least squares regression achieved a high precision and accuracy (R² up to 0.94), with a low root mean square error of prediction and high predictive ability (Q² > 0.85), in predicting the intramuscular fatty acid composition from plasma samples. Overall, (i) animals consuming diets with a higher antioxidant capacity and rich in n-3 precursors (ether extract) have healthier fat profiles, and (ii) plasma fatty acid profiling can be a powerful method for monitoring meat quality. This approach provides farmers with a non-invasive tool to improve meat quality management and promote healthier beef products. Full article

Fast and precise monitoring of soil organic matter (SOM) during maize growth periods is crucial for real-time assessment of soil quality. However, the big challenge we usually face is that many agricultural soils are covered by crops or snow, and the bare soil period is short, which makes reliable SOM prediction complex and difficult. In this study, an unmanned aerial vehicle (UAV) was utilized to acquire multi-temporal hyperspectral images of maize across the key growth stages at the field scale. The auxiliary predictors, such as spectral indices (I), field management (F), plant characteristics (V), and soil properties (S), were also introduced. We used stepwise multiple linear regression, partial least squares regression (PLSR), random forest (RF) regression, and XGBoost regression models for SOM prediction, and the results show the following: (1) Multi-temporal remote sensing information combined with multi-source predictors and their combinations can accurately estimate SOM content across the key growth periods. The best-fitting model depended on the types of models and predictors selected. With the I + F + V + S predictor combination, the best SOM prediction was achieved by using the XGBoost model (R² = 0.72, RMSE = 0.27%, nRMSE = 0.16%) in the R3 stage. (2) The relative importance of soil properties, spectral indices, plant characteristics, and field management was 55.36%, 26.09%, 9.69%, and 8.86%, respectively, for the multiple periods combination. Here, this approach can overcome the impact of the crop cover condition by using multi-temporal UAV hyperspectral images combined with valuable auxiliary variables. This study can also improve the field-scale farmland soil properties assessment and mapping accuracy, which will aid in soil carbon sequestration and soil management. Full article

This study developed a rapid, non-destructive method for the quantitative detection of protein in cottonseed by integrating near-infrared (NIR) fiber spectroscopy with chemometric machine learning. The establishment of this method holds significant importance for the rational and efficient utilization of cottonseed resources, advancing research on the genetic improvement of cottonseed nutritional quality, and promoting the development of equipment for raw cottonseed protein detection. Fuzzy cottonseed samples from three varieties were collected, and their NIR fiber-optic spectra were acquired. Reference protein contents were measured using the Kjeldahl method. Spectra were denoised through preprocessing, after which informative wavelengths were selected by combining Uninformative Variable Elimination (UVE) with Competitive Adaptive Reweighted Sampling (CARS) and the Random Frog (RF) algorithm. Partial least squares regression (PLSR), least-squares support vector machine (LSSVM), and support vector regression (SVR) models were then constructed to predict protein content. Model performance was assessed using the coefficient of determination (R²), root-mean-square error (RMSE), residual predictive deviation (RPD), and range error ratio (RER). The results indicate that the standard normal variate (SNV) is the most effective preprocessing step. The best performance was achieved by the LSSVM model coupled with UVE + CARS, yielding R² = 0.8571, RMSE = 0.0033, RPD = 2.7078, and RER = 10.72, outperforming the PLSR and SVR counterparts. These findings provide technical support for the rapid detection of fuzzy cottonseed protein and lay the groundwork for the development of related detection equipment. Full article

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 (R²_cv) of 0.72 with a root mean square error of cross-validation (RMSE_cv) of 51.50 ppm. Full article

Visible near-infrared (VNIR) spectroscopy offers a cost-effective solution to quantify the spatiotemporal dynamics of soil organic carbon (SOC), especially in the context of rapid advances in spectra-based local modeling approaches using large-scale soil spectral libraries. And yet, direct temporal transferability of VNIR spectroscopic modeling (applying historical models to new spectral data) and its capability to monitor temporal changes in SOC remain underexplored. To address this gap, this study uses the LUCAS Soil dataset (2009 and 2015) from France to evaluate the effectiveness of localized spectral models in detecting SOC changes. Two local learning algorithms, memory-based learning (MBL) and GLOBAL-LOCAL algorithms, were adapted to integrate spectral and soil property similarities during local training set selection, while also incorporating LUCAS 2009 soil measurements (clay, silt, sand, CEC) as covariates. These adapted local learning algorithms were then compared against global partial least squares regression (PLSR). The results demonstrated that localized models substantially outperformed global PLSR, with MBL achieving the highest accuracy for croplands, grasslands, and woodlands (R² = 0.72–0.79, RMSE = 4.73–20.92 g/kg). Incorporating soil properties during the local learning procedure reduced spectral heterogeneity, leading to improved SOC prediction accuracy. This improvement was particularly pronounced after excluding organic soils from grasslands and woodlands, as evidenced by 13.3–21.1% decreases in the RMSE. Critically, for SOC monitoring, spectrally predicted SOC successfully identified over 70% of samples experiencing significant SOC changes (>10% loss or gain), effectively capturing the spatial patterns of SOC changes. This study demonstrated the potential of localized spectral modeling as a cost-effective tool for monitoring SOC dynamics, enabling efficient and large-scale assessments critical for sustainable soil management. Full article

Thymoquinone (TMQ) is the main therapeutic constituent in black cumin (Nigella sativa L.) seeds. Conventional quantification by high-performance liquid chromatography (HPLC) is accurate but unsuitable for large-scale screening. This study evaluated the potential of near-infrared reflectance spectroscopy (NIRS) as a rapid and non-destructive alternative. A multi-year dataset of 780 seed samples was analyzed, and robust calibration models were developed using modified partial least squares regression. Independent validation of a two-year calibration equation using samples from a third year yielded a high predictive performance (r² = 0.85; SEP = 1.18 mg g⁻¹). Adding part of the samples from the third year to the calibration contributed to further improvement in the prediction of the remaining samples, demonstrating the benefits of continuous equation updates. The calibration equation proved effective for selecting genotypes with high TMQ content, particularly when expanded with samples from the third year. Spectral analysis identified key wavelengths associated with TMQ content, with wavelengths around 2106 nm and 2254 nm being the most relevant. This work demonstrates the applicability of NIRS for rapid phenotyping of TMQ content in black cumin seeds. Full article