Search Results (803)

Tomatoes are globally esteemed not only for their nutritional value but also for their complex and appealing aroma, a key determinant of consumer preference. The present study aimed to comprehensively characterise the volatilomic fingerprints of three tomato species—Solanum lycopersicum L., S. lycopersicum var. cerasiforme, and S. betaceum—encompassing six distinct varieties, through the application of headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). A total of 55 volatile organic compounds (VOCs) spanning multiple chemical classes were identified, of which only 28 were ubiquitously present across all varieties examined. Carbonyl compounds constituted the predominant chemical family, with hexanal and (E)-2-hexenal emerging as putative key contributors to the characteristic green and fresh olfactory notes. Notably, esters were found to dominate the unique volatile fingerprint of cherry tomatoes, particularly methyl 2-hydroxybenzoate, while Kumato and Roma varieties exhibited elevated levels of furanic compounds. Multivariate statistical analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), demonstrated clear varietal discrimination and identified potential aroma-associated biomarkers such as phenylethyl alcohol, 3-methyl-1-butanol, hexanal, (E)-2-octenal, (E)-2-nonenal, and heptanal. Collectively, these findings underscore the utility of volatilomic fingerprint as a robust tool for varietal identification and quality control within the food industry. Full article

It is fundamental, yet challenging, to accurately predict water levels at hydrological stations located along the banks of an open channel river due to the complex interactions between different hydraulic structures. This paper presents a novel application for short-term multivariate prediction applied to hydrological variables based on a multivariate NARX model coupled to a nonlinear recursive Ensemble Kalman Filter (EnKF). The proposed approach is designed for two hydrological stations of the Atrato river in Colombia, where the variables, water level, water flow, and water precipitation, are correlated using a NARX model based on neural networks. The NARX model is designed to consider the complex dynamics of the hydrological variables and their corresponding cross-correlations. The short-term two-day water-level forecast is designed with a fourth-order NARX model. It is observed that the NARX model coupled with EnKF improves the robustness of the proposed approach in terms of external disturbances. Furthermore, the proposed approach is validated by subjecting the NARX–EnKF coupled model to five levels of additive white noise. The proposed approach employs metric regressions to evaluate the proposed model by means of the Root Mean Squared Error (RMSE) and the Nash–Sutcliffe model efficiency (NSE) coefficient. Full article

The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a unified nonlinear modal analysis framework integrating second-order analytical solutions with novel nonlinear indices. Validated across diverse systems (DC microgrids and grid-connected PV), the framework yields significant findings: (1) second-order solutions outperform linearization in capturing critical oscillation/damping distortions under realistic disturbances, essential for fault analysis; (2) nonlinear effects induce modal dominance inversion and generate governing composite modes; (3) key interaction mechanisms are quantified, revealing distinct voltage regulation pathways in DC microgrids and multi-path dynamics driving DC voltage fluctuations. This approach provides a systematic foundation for dynamic characteristic assessment and directly informs control design for power electronics-dominated grids. Full article

The non-metallic armored optoelectronic cable (NAOC) serves as a critical component in deep-sea scientific winch systems. Due to its low density and excellent corrosion resistance, it has been widely adopted in marine exploration. However, as the operational water depth increases, the NAOC is subjected to multi-layer winding on the drum, resulting in a cumulative temperature rise that can severely impair insulation performance and compromise the safety of deep-sea operations. To address this issue, this paper conducts temperature rise experiments on NAOCs using a distributed temperature sensing test rig to investigate the effects of the number of winding layers and current amplitude on their temperature rise characteristics. Based on the experimental results, an electromagnetic thermal multi-physics field coupling simulation model is established to further examine the influence of these factors on the maximum operation time of the NAOC. Finally, a multi-variable predictive model for maximum operation time is developed, incorporating current amplitude, the number of winding layers, and ambient temperature, with a fitting accuracy of 97.92%. This research provides theoretical and technical support for ensuring the safety of deep-sea scientific operations and improving the reliability of deep-sea equipment. Full article

Seven lager beers and seven non-alcoholic counterparts, marketed by the same producers, were analyzed for their total phenolic content (TPC), radical scavenging activity (RSA) towards the DPPH radical and ThioBarbituric Index (TBI). All beers were also subjected to spin trapping experiments at 60 °C in the presence of PBN. To our knowledge, this is the first time that non-alcoholic beers (NABs) have been subjected to spin trapping experiments coupled with Electron Spin Resonance (ESR) spectroscopy. The evolution of the intensity of the PBN radical adducts during the first 150 min was represented graphically and the intensity at 150 min (I₁₅₀) and the area under the curve (AUC), were measured. The I₁₅₀ and the AUC of lagers and NABs are significantly different, whereas the TPC, the EC₅₀ of the DPPH assay, and the TBI of the two groups are superimposed. A relationship, previously proposed by us, to correlate ESR spectroscopy parameters with others obtained from UV-Vis spectrophotometry, was also applied, demonstrating its practicability. Multivariate analysis shows that clustering in two separate groups occurs only if I₁₅₀ and AUC are included in the model. Based on these results, ESR spectroscopy can be applied to study the oxidative stability of NABs. Full article

It is difficult for a single feature in wind power data to fully reflect the multifactor coupling relationship with wind power, while the forecast model hyperparameters rely on empirical settings, which affects the prediction accuracy. In order to effectively predict the continuous power in the future time period, an ultra-short-term prediction model of wind power based on fused features and an improved convolutional neural network (CNN) is proposed. Firstly, the historical power data are decomposed using dynamic modal decomposition (DMD) to extract their modal features. Then, considering the influence of meteorological factors on power prediction, the historical meteorological data in the sample data are extracted using kernel principal component analysis (KPCA). Finally, the decomposed power modal and the extracted meteorological components are reconstructed into multivariate time-series features; the snow ablation optimisation algorithm (SAO) is used to optimise the convolutional neural network (CNN) for wind power prediction. The results show that the root-mean-square error of the prediction result is 31.9% lower than that of the undecomposed one after using DMD decomposition; for the prediction of the power of two different wind farms, the root-mean-square error of the improved CNN model is reduced by 39.8% and 30.6%, respectively, compared with that of the original model, which shows that the proposed model has a better prediction performance. Full article

Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 and Sentinel-2 remote sensing data. Dual-polarization parameters (VV + VH and VV × VH) were constructed and tested. Pearson correlation analysis showed that these polarization combinations carried the most useful information for soil moisture estimation. We then applied Shapley Additive exPlanations (SHAP) for feature selection, and a Stacking model was used to perform soil moisture inversion based on the selected features. SHAP values derived from the coupled support vector regression (SVR) and random forest regression (RFR) models were used to select an additional six key features for model construction. Building on this framework, a comparative analysis was conducted to evaluate the predictive performance of multivariate linear regression (MLR), RFR, SVR, and a Stacking model that integrates these three models. The results demonstrate that the Stacking model outperformed other approaches in soil moisture retrieval, achieving a higher R² of 0.70 compared to 0.52, 0.61, and 0.62 for MLR, RFR, and SVR, respectively. This process concluded with the use of the Stacking model to generate a county-level farmland soil moisture distribution map, which provides an objective and practical approach to guide agricultural management and the optimized allocation of water resources in arid regions. Full article

The measurement of atmospheric moisture content is essential for the monitoring of severe weather events and hydrological studies. This paper proposes a multivariate linear regression correction model that integrates elevation data with Global Navigation Satellite System (GNSS)-derived precipitable water vapor (PWV) to refine the water vapor content based on FY-4A satellite remote sensing data, thereby improving its accuracy. Taking Hong Kong as an experimental area, we investigated the correlation between GNSS PWV and FY-4A PWV, confirming the feasibility of utilizing GNSS PWV to calibrate FY-4A PWV. Subsequently, by examining the differences between the two PWV values, we found that the elevation of the stations affects the consistency of PWV measurement. Based on this finding, the elevation data are introduced to construct a multivariate linear regression correction model with a first-order polynomial. To evaluate the performance of the proposed model, a comparison with other correction models is made, including second-order polynomials and power functions. The results indicate that the elevation-integrated water vapor correction model improves the root mean square error (RMSE) by 27.4% and the MAE by 26.7%, and reduces the bias from 0.592 to nearly 0. Its accuracy surpasses that of second-order polynomial and power function models, demonstrating a considerable improvement in the precision of FY-4A. Full article

A novel discrete-time internal model control (IMC) method cascaded with a discrete-time equal-order fractional Butterworth (EFBW) filter is proposed for multivariable systems with time-delay and non-minimum-phase (NMP) zeros. This is the first attempt to design such a control scheme in the discrete-time domain, as previous work has typically focused on continuous-time systems. An inverted decoupling (ID) method is introduced and integrated with the discrete-time IMC controller, forming a discrete-time ID-IMC scheme that mitigates coupling effects among control loops. Additionally, a discrete-time EFBW filter is designed to balance flexibility and design complexity effectively, with technical specifications guiding the determination of the filter’s optimal order. Structured singular value analysis is conducted to guarantee the stability and robustness of the resulting closed-loop system. Illustrative examples are provided, demonstrating the effectiveness and advantages of the proposed control method. Full article

Background/Objectives: Right ventricular failure (RVF) is a major contributor to early mortality after left ventricular assist device (LVAD) implantation. While various markers of right ventricular function and right ventriculoarterial coupling have been proposed, their value in predicting in-hospital mortality remains unclear. This study aimed to investigate the prognostic significance of the right atrial pressure/pulmonary capillary wedge pressure (RAP/PCWP) ratio—a surrogate of RV–pulmonary artery (PA) coupling—for in-hospital mortality following LVAD implantation. Methods: This retrospective single-center study included 44 patients who underwent LVAD implantation. Preoperative clinical, echocardiographic, and invasive hemodynamic parameters were collected. The optimal RAP/PCWP ratio cut-off was determined using receiver operating characteristic (ROC) analysis. Predictors of in-hospital mortality were assessed using univariate and multivariate logistic regression. Results: Patients were stratified into high (≥0.47) and low (<0.47) RAP/PCWP ratio groups. In-hospital mortality was significantly higher in the high RAP/PCWP group (46% vs. 10%, p = 0.020). The optimal cut-off for the RAP/PCWP ratio was 0.47 (AUC: 0.829). In multivariate analysis, RAP/PCWP ratio (OR: 3.48 per 0.1 increase, p = 0.020) and INTERMACS 1–2 profile (OR: 39.19, p = 0.026) were independent predictors of in-hospital mortality. Conclusions: Preoperative RAP/PCWP ratio, as a surrogate of right ventriculoarterial coupling, independently predicts in-hospital mortality following LVAD implantation. Its incorporation into preoperative assessment may enhance risk stratification and guide clinical management in this high-risk population. Full article

Determining the soil’s nitrogen supply accurately and quickly is essential for effective agricultural management. This study explores the use of near-infrared (NIR) spectroscopy combined with spectral pre-processing techniques (such as Savitzky–Golay filtering) and partial least squares regression (PLSR) to assess soil nitrogen levels. Six soil types of varying compositions, treated with different levels of Urea-N fertilizer, were examined. Nitrogen-specific NIR peaks were identified, and regression models were consequently developed. Through a comparison of the performance of the models, the most effective model for nitrogen detection was selected. In calibration, the models performed well, with high R² (over 0.9) and low root mean square error (RMSE) values. The second derivative-based (SD) model slightly outperformed the first derivative-based (FD) model in terms of accuracy. Both models showed minimal bias, indicating reliable performance. During validation, the FD model outperformed the SD model in terms of R², root mean square error of prediction (RMSEP), and residual prediction deviation (RPD). Thus, the FD model demonstrated good predictive ability (R² = 0.77, RPD = 2.06), while the SD model was less effective (R² = 0.65, RPD = 1.77). Compared to previous studies, this study uniquely combines real-time online detection capability with low computational cost, unlike most prior offline approaches, and includes model validation across various soil types. Overall, NIR spectroscopy coupled with multivariate models proves to be a promising tool for the detection of nitrogen levels in various soils. Full article

Tracking control of a quadrotor–slung load system is extremely challenging due to its under-actuation property, couple effects, and various uncertainties. This work proposes a composite backstepping control framework combining command filter control and a multivariable finite-time disturbance observer to ensure robust position and orientation control for aerial payload transportation with high precision. Firstly, the kinematic and dynamic model under perturbations is derived based on Newton’s second law. The thrust control force consists of two orthogonal parts, each dedicated to regulating the position and orientation of the slung load independently. Then, hierarchical backstepping control generates the two parts in the load-translation and the load-orientation subsystems. Command filters are introduced into nonlinear backstepping to smoothen the control signals and overcome the problem of explosion of complexity. Additionally, to counteract the adverse effect of perturbations emerging in the linear velocity and angular velocity loops, multivariable finite-time observers are developed to ensure the estimation errors converge within a finite time horizon. Finally, comparative numerical simulation results validate the efficacy of the developed quadrotor–slung load tracking controller. Simulation results show that the proposed controller achieves smaller position tracking and orientation errors compared to traditional methods, demonstrating robust disturbance rejection and high-precision control. Full article

This study aimed to determine the prevalence of type 1 diabetes mellitus (T1DM) in French Guiana and describe the social profiles of the patients. We conducted a multicenter cross-sectional study of children under 18 years who were diagnosed with type 1 diabetes and followed up from 2002 to 2021. Over a 20-year period, 48 children under 18 years with type 1 diabetes living in French Guiana were included in the study, out of a total of 59 cases. There were 26 girls and 22 boys. The median age at diagnosis was 8.52 years [IQR 6–12]. The incidence rate was 5.9 per 100,000 people in children aged 0–18 years. The 5–9-year age group was the most affected 43.7% (95% CI 38–51%). Of these children, 56.2% (95% confidence interval 40–70%) lived in single-parent households, and 35% (95% CI 23–57%) of the parents had a primary education. Of the children, 29% (95% CI 21–42%) were from families with no resources. Diabetes was diagnosed by ketoacidosis in 56% (95% CI 38–74%) of the patients. Forty percent (95% CI 35–66%) of the patients had an HbA1c > 9%. There was an imbalance in the prevalence of children with higher Hba1c (>9%), with 18.7% (95% CI 10–29%, p < 0.001) of children whose parents had a low level of education having an Hba1c > 9% compared with only 6% (95% CI 3–10%) of children whose parents had a university degree, and a marked imbalance in the prevalence of children with High Hba1c (>9%) among children from single-parent families (22.9%, 95% CI 17–30%) compared with children whose parents lived in couples (8%, 95% CI 5–12%). The 10–14-year age group (18.7%, 95% CI 11–25%) had the highest imbalance in the prevalence of poor diabetes control between children whose parents had lower versus higher education levels. Diabetic retinopathy and diabetic nephropathy were the only reported complications. The multivariate analysis showed that a low level of parental education (Odds ratio 2.9 [95% CI 2.1–4.5], p < 0.001) and single-parent families (Odds ratio 3.1 [95% CI 2.6–4.3], p < 0.001) were predictors of poor control of T1DM. However, the lack of social insurance coverage at diagnosis was not associated with poor T1DM control (p = 0.4). In conclusion, these sociodemographic factors should be considered when caring for children with T1DM in French Guiana. Full article

Marine risers are essential for offshore resource extraction, yet traditional metal risers encounter limitations in deep-sea applications due to their substantial weight. Fiber-reinforced polymer (FRP) composites offer a promising alternative with advantages including low density and enhanced corrosion/fatigue resistance. However, FRP risers remain susceptible to fatigue damage from vortex-induced vibration (VIV). Therefore, this study investigated VIV behavior of FRP composite risers considering the coupled effect of tensile-flexural moduli, top tensions, slenderness ratios, and flow velocities. Through an orthogonal experimental design, eighteen cases were analyzed using multivariate nonlinear fitting. Results indicated that FRP composite risers exhibited larger vibration amplitudes than metal counterparts, with amplitudes increasing to both riser length and flow velocity. It was also found that the optimized FRP configuration demonstrated enhanced fiber strength utilization. Parameter coupling analysis revealed that the multivariate nonlinear fitting model achieved sufficient accuracy when incorporating two coupled parameters, with the most significant interaction occurring between flexural modulus and top tension. Full article

The production of Fiore Sardo cheese is regulated by the specification of the Protected Designation of Origin (PDO), which aims to guarantee the specific area of production, the know-how of local producers, and the specific use of raw milk from Sarda sheep. The thermization of milk is a sub-pasteurization process that is commonly used in cheese-making to lower the bacterial load and increase the shelf life of the product; it is therefore a cause of non-compliance with the PDO specification of Fiore Sardo cheese, allowing producers to gain practical and economic advantages. In this work, NIR spectroscopy coupled with multivariate discriminant analysis was used to identify the thermal treatment of milk in Fiore Sardo cheese samples. Cheeses were produced using raw milk (38 °C), low-thermized milk (57 °C for 30 s), and high-thermized milk (68 °C for 30 s). The NIR spectra of the cheeses were used to build discriminant models for individuating the thermal treatment of the processed milk. The obtained discriminant models were able to correctly classify about 90% of the Fiore Sardo cheese samples. This method could be suitable as a screening technique to authenticate Fiore Sardo PDO cheese. Full article