Search Results (5,792)

This study focuses on the optimization of modern extraction techniques for selected by-product materials, including apple, lemon, and tangerine peels, and onion skins, using artificial neural network (ANN) models. The extraction methods included ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) with water as the extractant, as well as maceration (MAC) with natural deep eutectic solvents (NADES). Key parameters, such as total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities, including reducing power (EC₅₀) and free radical scavenging capacity (IC₅₀), were evaluated to compare the efficiency of each method. Among the techniques, UAE outperformed both MAE and MAC in extracting bioactive compounds, especially from onion skins and tangerine peels, as reflected in the highest TPC, TFC, and antioxidant activity. UAE of onion skins showed the best performance, yielding the highest TPC (5.735 ± 0.558 mg CAE/g) and TFC (1.973 ± 0.112 mg RE/g), along with the strongest antioxidant activity (EC₅₀ = 0.549 ± 0.076 mg/mL; IC₅₀ = 0.108 ± 0.049 mg/mL). Tangerine peel extracts obtained by UAE also exhibited high phenolic content (TPC up to 5.399 ± 0.325 mg CAE/g) and strong radical scavenging activity (IC₅₀ 0.118 ± 0.099 mg/mL). ANN models using multilayer perceptron architectures with high coefficients of determination (r² > 0.96) were developed to predict and optimize the extraction results. Sensitivity and error analyses confirmed the robustness of the models and emphasized the influence of the extraction technique and by-product type on the antioxidant parameters. Principal component and cluster analyses showed clear grouping patterns by extraction method, with UAE and MAE showing similar performance profiles. Overall, these results underline the potential of UAE- and ANN-based modeling for the optimal utilization of agricultural by-products. Full article

Non-invasive glucose monitoring remains a significant challenge in diabetes management, with existing approaches often limited by poor accuracy, high cost, or patient discomfort. Microwave-based biosensors offer a promising label-free alternative by exploiting the dielectric contrast between glucose and water. This paper presents a compact, dual-band concentric square-shaped split-ring resonator (SRR-type) biosensor fabricated on a low-cost FR-4 substrate for aqueous glucose detection. The sensor leverages electric field confinement in inter-ring gaps to transduce glucose-induced permittivity changes into measurable shifts in resonance frequency and reflection coefficient. Experimental results demonstrate a linear, monotonic response across the clinical range up to 250 mg/dL, with a frequency-domain sensitivity of 1.964 MHz/(mg/dL) and amplitude-domain sensitivity of 0.0332 dB/(mg/dL), achieving high coefficients of determination (R² = 0.9956 and 0.9927, respectively). The design achieves a normalized size of 0.137 λ_g², combining high sensitivity and compact size within a scalable platform. Operating in the UWB-adjacent band (2.76–3.25 GHz), the proposed biosensor provides a practical, reproducible, and PCB-compatible solution for next-generation label-free glucose monitoring. Full article

Objectives: This study aimed to examine the association between the visceral adiposity index and arterial stiffness in healthy adults via original data from the EVasCu study and to contextualize these findings through a meta-analysis of previously published studies in the general population. Methods: A cross-sectional analysis was conducted in 389 healthy adults from the EVasCu study. The visceral adiposity index was calculated on the basis of waist circumference, body mass index, triglycerides, and high-density lipoprotein cholesterol, integrating the anthropometric and metabolic components of visceral adiposity. Arterial stiffness was assessed by the aortic pulse wave velocity. These original findings were complemented by a meta-analysis, including EVasCu data and data from prior studies, to obtain pooled correlation coefficients and 95% confidence intervals (CIs) for the association between visceral adiposity and arterial stiffness. Results: In the EVasCu study, the visceral adiposity index showed a statistically significant moderate correlation with the aortic pulse wave velocity (r = 0.281, p < 0.001). In the meta-analysis, the pooled correlation coefficient was 0.34 (95% CI: 0.27, 0.42), supporting a consistent association between the visceral adiposity index and both central and peripheral arterial stiffness across diverse populations. Conclusions: These findings indicate a positive association between the visceral adiposity index and arterial stiffness in both healthy individuals and populations with cardiometabolic conditions. However, given the predominantly cross-sectional nature of the evidence and the heterogeneity among the included studies, the results should be interpreted with caution. Further longitudinal, multivariable, and mechanistic studies are needed to clarify the clinical relevance of the visceral adiposity index beyond correlation and to determine its potential role as a complementary marker in cardiovascular risk assessment. Full article

Frequent cloud cover severely limits the use of optical remote sensing for continuous ecological monitoring. Synthetic aperture radar (SAR) offers an all-weather alternative, but translating SAR data to optical equivalents is challenging, particularly in cloudy regions where paired training data are scarce. To address this, we developed an enhanced CycleGAN (denoted by SA-CycleGAN) to derive a high-fidelity, temporally continuous normalized difference vegetation index (NDVI) from SAR imagery. The SA-CycleGAN introduces a novel spatiotemporal attention generator that dynamically computes global and local feature relationships to capture long-range spatial dependencies across diverse landscapes. Furthermore, a structural similarity (SSIM) loss function is integrated into the SA-CycleGAN to preserve the structural and textural integrity of the synthesized images. The performance of the SA-CycleGAN and three unsupervised models (DualGAN, GP-UNIT, and DCLGAN) was evaluated by deriving NDVI time series from Sentinel-1 SAR images across four sites with different vegetation types. Ablation experiments were conducted to verify the contributions of the key components in the SA-CycleGAN model. The results demonstrate that the SA-CycleGAN significantly outperformed the comparison models across all four sites. Quantitatively, the proposed method achieved the lowest Root Mean Square Error (RMSE) of 0.0502 and the highest Coefficient of Determination (R²) of 0.88 at the Zhangbei and Xishuangbanna sites, respectively. The ablation experiments confirmed that the attention mechanism and SSIM loss function were crucial for capturing long-range features and maintaining spatial structure. The SA-CycleGAN proves to be a robust and effective solution for overcoming data gaps in optical time series. Full article

Change in direction ability (COD) is a fitness component that may be related to safe and effective participation in pickleball. The general aim of the research was to examine a COD test that may be specific to the movement demands of the sport. Therefore, we tested the inter-trial reliability of the modified spider test for pickleball, compared learning effects between younger and older adults, and examined the reliability and validity of hand timing compared to timing gates. In this cross-sectional study, 36 participants (ages 19–78) were grouped as adults (ages 18–49) or seniors (ages 50+) according to the USA Pickleball age groupings. Participants completed a standard warm-up, one practice trial, and five full-effort trials with 4–6 min of rest between trials. Intraclass correlation coefficient (ICC) was used to determine reliability across five trials. Inter-rater reliability and validity of hand timing were also examined with ICCs. Pairwise comparison t-tests of individual trials were performed using the Hochberg method to determine learning effect. Linear regression analyses were used to determine if any segment could predict total trial time. During participation, older players provided unsolicited feedback that they were concerned about the safety of the backpedaling in the spider test. We observed that one person fell while backpedaling, though suffered no injury. Results indicate that the spider test was reliable across all five trials (ICC = 0.977). A learning effect was detected between the first and second trial (p = 0.001), and the magnitude of the effect was significantly different between age groups (p = 0.009). Hand timing demonstrated excellent inter-rater reliability (ICC = 0.993) and validity (ICC = 0.990). Splits 2, 3, and 4 significantly predicted total test time (R² = 0.973, 0.973, and 0.986, respectively). The test demonstrated reliability, but older players expressed concern about backpedaling. This raises questions about backpedaling safety in pickleball. Therefore, we do not recommend this test. Future research needs to determine appropriate tests to screen for fall risk in the dynamic movements relevant to pickleball. Full article

The study presents an approach to predicting the hysteresis behavior of shape memory alloys (SMAs) using recurrent neural networks, including SimpleRNN, LSTM, and GRU architectures. The experimental dataset was constructed from 100 to 250 loading–unloading cycles collected at seven loading frequencies (0.1, 0.3, 0.5, 1, 3, 5, and 10 Hz). The input features included the applied stress σ (MPa), the cycle number N (the Cycle parameter), and the indicator of the loading–unloading stage (UpDown). The output variable was the material strain ε (%). Data for training, validation, and testing were split according to the group-based principle using the Cycle parameter. Eighty percent of cycles were used for model training, while the remaining 20% were reserved for independent assessment of generalization performance. Additionally, 10% of the training portion was reserved for internal validation during training. Model accuracy was evaluated using MAE, MSE, MAPE, and the coefficient of determination R². All architectures achieved R² > 0.999 on the test sets. Generalization capability was further assessed on fully independent cycles 251, 260, 300, 350, 400, 450, and 500. Among all architectures, the LSTM network showed the highest accuracy and the most stable extrapolation, consistently reproducing hysteresis loops across frequencies 0.1–3 Hz and 10 Hz, whereas the GRU network showed the best performance at 5 Hz. Model interpretability using the Integrated Gradient (IG) method revealed that Stress is the dominant factor influencing the predicted strain, contributing the largest proportion to the overall feature importance. The UpDown parameter has a stable but secondary role, reflecting transitions between loading and unloading phases. The influence of the Cycle feature gradually increases with the cycle number, indicating the model’s ability to account for the accumulation of material fatigue effects. The obtained interpretability results confirm the physical plausibility of the model and enhance confidence in its predictions. Full article

The leaf area index (LAI) serves as a critical parameter for assessing wetland ecosystem functions, and accurate LAI retrieval holds substantial significance for wetland conservation and ecological monitoring. To address the spatial constraints of traditional ground-based measurements and the limited accuracy of single-source remote sensing data, this study utilized unmanned aerial vehicle (UAV)-borne hyperspectral and LiDAR sensors to acquire high-quality multi-source remote sensing data of coastal wetlands in the Yellow River Delta. Three machine learning algorithms—random forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—were employed for LAI retrieval modeling. A total of 38 vegetation indices (VIs) and 12-point cloud features (PCFs) were extracted from hyperspectral imagery and LiDAR point cloud data, respectively. Pearson correlation analysis and the Shapley Additive Explanations (SHAP) method were integrated to identify and select the most informative VIs and PCFs. The performance of LAI retrieval models built on single-source features (VIs or PCFs) or multi-source feature fusion was evaluated using the coefficient of determination (R²) and root mean square error (RMSE). The main findings are as follows: (1) Multi-source feature fusion significantly improved LAI retrieval accuracy, with the RF model achieving the highest performance (R² = 0.968, RMSE = 0.125). (2) LiDAR-derived structural metrics and hyperspectral-derived vegetation indices were identified as critical factors for accurate LAI retrieval. (3) The feature selection method integrating mean absolute SHAP values (|SHAP| values) with Pearson correlation analysis enhanced model robustness. (4) The intertidal zone exhibited pronounced spatial heterogeneity in the vegetation LAI distribution. Full article

Large-scale wireless sensor networks with electric field energy harvesters (EFEHs) offer self-powered, eco-friendly, and scalable crop monitoring in hydroponic greenhouses. However, their practical adoption is limited by the low power density of current EFEHs, which restricts the reliable operation of external sensors. To address this challenge, this work presents a noninvasive EFEH assembled with hydroponic leafy vegetables that harvests electric field energy and estimates plant functional traits directly from the electrical response. The device operates through electrostatic induction produced by an external alternating electric field, which induces surface charge redistribution on the leaf. These charges are conducted through an external load, generating an AC voltage whose amplitude depends on the dielectric properties of the leaf. A low-voltage prototype was designed, built, and evaluated under controlled electric field conditions. Two representative species, Beta vulgaris (chard) and Lactuca sativa (lettuce), were electrically characterized by measuring the open-circuit voltage (VOC) and short-circuit current (ISC) of EFEHs. Three regression models were developed to determine the relationship between foliar moisture content (FMC) and fresh mass with electrical parameters. Empirical results disclose that the plant functional traits are critical predictors of the electrical output of EFEHs, achieving coefficients of determination of $R^2=0.697$ and $R^2=0.794$ for each species, respectively. These findings demonstrate that EFEHs can serve as self-powered, noninvasive indicators of plant physiological state in living leafy vegetable crops. Full article

Reinforced concrete buildings with masonry-induced soft-storey irregularities exhibit extreme seismic vulnerability, a critical risk often underestimated by conventional code-based design. Standard equivalent static methods typically fail to capture the intense concentration of seismic demand at the flexible ground level, leading to unconservative designs that do not meet performance objectives. This research proposes a corrective linear–static methodology to address this deficiency. A new Equivalent Lateral Force profile (ELF_i1) was developed, derived from modal analyses of 235 representative soft-storey archetypes to accurately account for stiffness heterogeneity. This profile was integrated with a realistic response modification coefficient (R_i1 = 5.04), determined to be 37% lower than the normative R-factor (R = 8) prescribed by code. Nonlinear static analyses confirmed that conventional design resulted in “irreparable” damage (mean Global Damage Index = 0.82). In contrast, redesigning the structure using the proposed ELF_i1 and R_i1 methodology successfully mitigated damage concentration, upgrading structural performance to a “repairable” state (mean Global Damage Index = 0.52). Finally, Incremental Dynamic Analysis validated the approach; the redesigned structure satisfied FEMA P695 collapse prevention criteria, achieving an Adjusted Collapse Margin Ratio (ACMR) of 2.10. This study confirms the proposed method is a robust and practical design alternative for soft-storey mechanisms within a simplified linear framework. Full article

The development of rapid and intelligent methods is urgently needed for wheat quality evaluation. Using the prediction of wet gluten content as a case study, this work systematically investigated the performance of various machine learning algorithms and their optimization for content prediction, based on hyperspectral data from the visible and near-infrared ranges of wheat grains and flour. The results revealed that the random forest regression (RFR) algorithm delivered the best predictive performance under two conditions: first, when applied directly to visible spectra; and second, when applied to fused visible and near-infrared spectral data. This held true for both grains and flour. Conversely, its direct application to NIR spectra alone yielded relatively worse performance. Following data optimization, the first-derivative (FD) visible spectra of wheat grains were smoothed using a Savitzky–Golay (SG) filter and subsequently used as input for the RFR model. This optimized approach achieved a coefficient of determination (r²) of 0.8579, a root mean square error (RMSE) of 0.0216, and a relative percent deviation (RPD) of 2.6978. Under the same conditions, for wheat flour, the corresponding values were 0.8383, 0.0231, and 2.5293, respectively. Similarly, for wheat flour, the RFR model was applied to the SG-filtered FD spectra derived from the fused visible and near-infrared data, yielding an r² of 0.8474, an RMSE of 0.0224, and an RPD of 2.6034. Under the same conditions, wheat grains yielded an r² of 0.8494, an RMSE of 0.0223, and an RPD of 2.6208. This efficient and rapid intelligent prediction scheme demonstrates considerable potential for the quality assessment and control of relevant food products. Full article

In the present study, thin-layered core–shell Gd₂O₃@SiO_1.5R (R is C₃H₆NH₂) structures were synthesized by gas-phase surface modification of a Gd₂O₃ core with a 3-aminopropyltriethoxysilane (APTES) shell for the first time. The proposed method consists of two consecutive steps carried out in a fixed-bed reactor. The first step involves APTES adsorption on the Gd₂O₃ surface, followed by APTES hydrolysis by water vapor. The organosyloxane shell formation was confirmed by transmission and scanning electron microscopy, IR spectroscopy, and thermogravimetric data. X-ray attenuation coefficients of Gd₂O₃ and Gd₂O₃@SiO_1.5R samples were determined by photon-counting computed tomography in a phantom study. The SiO_1.5R shells in the synthesized Gd₂O₃@SiO_1.5R samples had minimal thickness and did not affect the attenuation coefficients of Gd₂O₃. Full article

Tight reservoirs are highly heterogeneous, with complex pore-throat structures and varying fluid occurrences. The Archie equation shows a nonlinear relationship, making traditional logging interpretation methods unreliable for accurately predicting water saturation. This paper employs particle swarm optimization (PSO), using Pearson correlation coefficient-based feature selection, to compare the accuracy of three machine learning algorithms: XGBoost, LightGBM, and MERF in predicting water saturation in tight reservoirs. It also applies the SHAP value algorithm to provide a visual and interpretive analysis of the PSO LightGBM model. The research results indicate that the root mean square error (RMSE), coefficient of determination (R2), and accuracy of water saturation (Swa) of the PSO-LightGBM model on the training and test sets are 0.955, 3.087, 91.8%, and 0.89, 5.132, 85.2%, respectively. Interpretability analysis using SHAP values reveals that the five normalized logging parameters—SP, M2R3, DEN, DT, and CN—are the most influential features in the water saturation prediction model. In application examples involving water saturation prediction across eight sections of tight reservoirs in the study area, the PSO–LightGBM, PSO–XGBoost, and PSO–MERF models achieved Swa of 88.9%, 80.3%, and 87.8%, respectively. The results demonstrate that the PSO–LightGBM model is a reliable and efficient method for predicting water saturation, with significant practical potential. Full article

Studies have established that extreme air pollution is more prevalent and is responsible for more deaths and disability-adjusted life years (DALY) in urban cities, especially in developing economies. However, the paucity of ground-based observation has greatly hindered extensive and long-term monitoring and, as such, a good understanding of the trend and characteristics of air quality where it matters most. Aerosol optical depth (AOD) from satellites retrievals provides good spatial and temporal resolutions of atmospheric aerosols and could be a good proxy for ground-level PM_2.5 concentration. This study used a Bayesian regression model to determine the parameters of a PM_2.5 model at four monitoring stations using AOD and selected atmospheric variables (PBLH and RH) as input. The dry-air reference value (K) and the integrated humidity coefficient (γ) were used to delineate the effects of the aerosol characteristics. The values of K and γ, $0.02<K<0.07$ (${\mathrm{m}}^2{\mathrm{g}}^{\mathrm{-1}}$) and $0.54<\gamma <3.14$, respectively, are site-specific even within the same country as is the case for Lekki and Benin (both in Nigeria). The PM_2.5 estimates from the developed observation-based model were in good agreement with the ground-based observations ($0.55<r<0.77$). RH and a combination of PBLH-RH were the best performers in the development of the model. Firstly, this study identifies the unique range of values for K and $\gamma$ for site-classes in the sub-Saharan tropical climate. Secondly, PBLH adds more explanatory power to the PM_2.5 estimates in Benin and Douala (both non-coastal cities) while RH improves the performance of the model significantly in Lekki and Owendo (both coastal cities). For West Africa and similar data-sparse regions, the methodology presented here offers a practical pathway to enhance air quality monitoring capabilities. Full article

Climate-induced flooding is a major issue throughout the globe, resulting in damage to infrastructure, loss of life, and the economy. Therefore, there is an urgent need for sustainable flood risk management. This paper assesses the effectiveness of the hybrid defense system using advanced artificial intelligence (AI) techniques. A data series of energy dissipation (ΔE), flow conditions, roughness, and vegetation density was collected from literature and laboratory experiments. Out of the selected 136 data points, 80 points were collected from literature and 56 from a laboratory experiment. Advanced AI models like Random Forest (RF), Extreme Boosting Gradient (XGBoost) with Particle Swarm Optimization (PSO), Support Vector Regression (SVR) with PSO, and artificial neural network (ANN) with PSO were trained on the collected data series for predicting floodwater energy dissipation. The predictive capability of each model was evaluated through performance indicators, including the coefficient of determination (R²) and root mean square error (RMSE). Further, the relationship between input and output parameters was evaluated using a correlation heatmap, scatter pair plot, and HEC-contour maps. The results demonstrated the superior performance of the Random Forest (RF) model, with a high coefficient of determination (R² = 0.96) and a low RMSE of 3.03 during training. This superiority was further supported by statistical analyses, where ANOVA and t-tests confirmed the significant performance differences among the models, and Taylor’s diagram showed closer agreement between RF predictions and observed energy dissipation. Further, scatter pair plot and HEC-contour maps also supported the result of SHAP analysis, demonstrating greater impact of the roughness condition followed by vegetation density in reducing floodwater energy dissipation under diverse flow conditions. The findings of this study concluded that RF has the capability of modeling flood risk management, indicating the role of AI models in combination with a hybrid defense system for enhanced flood risk management. Full article

Current machine learning-based gross primary productivity (GPP) upscaling error correction approaches exhibit two critical limitations: (1) failure to account for nonuniform density distributions of sub-pixel heterogeneity factors during upscaling and (2) dependence on subjective classification thresholds for characterizing factor variations. These shortcomings reduce accuracy and limit transferability. To address these issues, we propose an equidistant sampling method with optimized step size and intervals that precisely quantifies nonuniform density distributions and enhances correction precision. We validate our approach by applying it to correct 480 m resolution GPP simulations generated from an eco-hydrological model, with performance evaluation against 30 m resolution benchmarks using determination coefficient (R²) and root mean square error (RMSE). The proposed method demonstrates a significant improvement over previous elevation-based correction research (baseline R² = 0.48, RMSE = 285 gCm⁻²yr⁻¹), achieving a 0.27 increase in R² and 91.22 gCm⁻²yr⁻¹ reduction in RMSE. For comparative analysis, we implement k-means clustering as an alternative geostatistical method, which shows lesser improvements (ΔR² = 0.21, ΔRMSE = −63.54 gCm⁻²yr⁻¹). Crucially, when using identical statistical interval counts, our optimized-step equidistant sampling method consistently surpasses k-means clustering in performance metrics. The optimal-step equidistant sampling method, paired with appropriate interval selection, offers an efficient solution that maintains high correction accuracy while minimizing computational costs. Controlled variable experiments further revealed that the most significant factors affecting GPP upscaling error correction are land cover, altitude, slope, and TNI, trailed by LAI, whereas slope orientation, SVF, and TWI hold equal relevance. Full article