Search Results (2,544)

One of the leading causes of climate change and global warming is the rise in carbon dioxide (${\mathrm{CO}}_2$) levels. For a precise assessment of ${\mathrm{CO}}_2$’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing ${\mathrm{CO}}_2$ sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) ${\mathrm{XCO}}_2$ retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled ${\mathrm{CO}}_2$ concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 ${\mathrm{km}}^2$) ${\mathrm{XCO}}_2$. When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and ${\mathrm{R}}^2$ of 0.90. Full article

Quercetin, a key flavonoid in Anoectochilus roxburghii (Wall.) Lindl., plays an important role in determining the pharmacological value of this medicinal herb. However, traditional methods for quercetin quantification are destructive and time-consuming, limiting their application in real-time quality monitoring. This study investigates the hyperspectral response characteristics of quercetin using near-infrared hyperspectral imaging and establishes a feature-based model to explore its detectability in A. roxburghii leaves. We scanned standard quercetin solutions of known concentration under the same imaging conditions as the leaves to produce a dilution series. Feature-selection methods used included the successive projections algorithm (SPA), Pearson correlation, and competitive adaptive reweighted sampling (CARS). A 1D convolutional neural network (1D-CNN) trained on SPA-selected wavelengths yielded the best prediction performance. These key wavelengths—particularly the 923 nm band—showed strong theoretical and statistical relevance to quercetin’s molecular absorption. When applied to plant leaf spectra, the standard-trained model produced continuous predicted quercetin values that effectively distinguished cultivars with varying flavonoid contents. PCA visualization and ROC-based classification confirmed spectral transferability and potential for functional evaluation. This study demonstrates a non-destructive, spatially resolved, and biochemically interpretable strategy for identifying bioactive markers in plant tissues, offering a methodological basis for future hyperspectral inversion studies and intelligent quality assessment in herbal medicine. Full article

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article

Functional beverages enhance the nutritional value of their ingredients by increasing the levels of bioactive components, such as probiotics. To achieve consumer acceptance, functional beverages must be both palatable and nutritious. This study investigates the fermentation of quinoa and cherimoya at two temperatures (25 °C and 32 °C) using water kefir grains. The aim was to create a fermented mix that is both balanced and appealing to consumers. The response variables measured were the concentrations of lactic acid bacteria (LAB) and yeasts (CFU mL⁻¹), as well as the overall liking (OL). Ten semi-trained panellists evaluated them using a seven-point hedonic scale. All three developed models for LAB and yeast growth, and OL exhibited R² values exceeding 0.8, indicating a strong model fit and simultaneous optimisation considering the three key responses. At a temperature of 25 °C, the mass fractions of the mixes containing quinoa puree (QP) and cherimoya juice (CJ) were 0.13 and 0.87, respectively. Under optimal conditions, the LAB and yeast increased by 4.2 and 4.4 log units, respectively. Moreover, a significant 62% increase in protein levels and a notable 82% decrease in ascorbic acid were observed after 48 h of fermentation, likely caused by the Maillard reaction. Full article

High-entropy alloys (HEAs) are highly concentrated, multicomponent alloys that have received significant attention due to their superior properties compared to conventional alloys. The mechanical properties and hardness are interrelated, and it is widely known that the hardness of HEAs depends on the principal alloying elements and their composition. Therefore, the desired hardness prediction to develop new HEAs is more interesting. However, the relationship of these compositions with the HEA hardness is very complex and nonlinear. In this study, we develop an artificial neural network (ANN) model using experimental data sets (535). The compositional elements—Al, Co, Cr, Cu, Mn, Ni, Fe, W, Mo, and Ti—are considered input parameters, and hardness is considered as an output parameter. The developed model shows excellent correlation coefficients (Adj R2) of 99.84% and 99.3% for training and testing data sets, respectively. We developed a user-friendly graphical interface for the model. The developed model was used to understand the effect of alloying elements on hardness. It was identified that the Al, Cr, and Mn were found to significantly enhance hardness by promoting the formation and stabilization of BCC and B2 phases, which are inherently harder due to limited active slip systems. In contrast, elements such as Co, Cu, Fe, and Ni led to a reduction in hardness, primarily due to their role in stabilizing the ductile FCC phase. The addition of W markedly increased the hardness by inducing severe lattice distortion and promoting the formation of hard intermetallic compounds. Full article

Planetary gearbox serves as a key transmission component in planetary ball screw actuator systems. Under the action of alternating loads, the stress concentration locations of the planet carrier in actuators with planetary gear trains are prone to fatigue cracks, which can lead to catastrophic system breakdowns. However, due to the complex vibration transmission path and the interference of uninterested vibration components, the characteristic modulation signal is ambiguous, so it is challenging to diagnose this fault. Therefore, this paper proposes a new fault diagnosis method. Firstly, a vibration signal model is established to accurately characterize the amplitude and phase modulation effects caused by cracked carriers, providing theoretical guidance for fault feature identification. Subsequently, three novel sideband evaluators of the modulation signal bispectrum (MSB) and their parameter selection ranges are proposed to efficiently locate the optimal fault-related bifrequency signatures and reduce computational cost, leveraging the effects identified by the model. Finally, a novel health indicator, the mean absolute root value (MARV), is used to monitor the state of the planet carrier. The effectiveness of this method is verified by experiments on the planetary gearbox test rig. The results show that the robustness of the amplitude and phase modulation effect of the cracked carrier in the low-frequency band is significantly higher than that in the high-frequency band, and the initial carrier crack can be accurately identified using this phenomenon under different operating conditions. This study provides a reliable solution for the condition monitoring and health management of the actuation system, which is helpful to improve the safety and reliability of operation. Full article

Basketball is a high-intensity, intermittent sport in which physical demands fluctuate depending on different contextual variables. Most studies addressed these demands in isolation without integrative approaches. Therefore, the present study aimed to identify key variables explaining players’ physical workload across game quarters and playing positions through principal component analysis (PCA). Ninety-four elite U18 male basketball players were registered during the EuroLeague Basketball ANGT Finals using WIMU PRO™ multi-sensor wearable devices that integrate local positioning systems (LPS) and microelectromechanical systems (MEMS). From over 250 recorded variables, 31 were selected and analyzed by PCA for dimensionality reduction, analyzing the effects of game quarter and playing position. Five to eight principal components explained 61–73% of the variance per game quarter, while between four and seven components explained 64–69% per playing position. High-intensity variables showed strong component loadings in early quarters, with explosive distance (loading = 0.898 in total game, 0.645 in Q1) progressively declining to complete absence in Q4. Position-based analysis revealed specific workload profiles: guards required seven components to explain 69.25% of the variance, with complex movement patterns, forwards showed the highest explosive distance loading (0.810) among all positions, and centers demonstrated concentrated power demands, with PC1 explaining 34.12% of the variance, dominated by acceleration distance (loading = 0.887). These findings support situational and individualized training approaches, allowing coaches to design individual training programs, adjust rotation strategies during games, and replicate demanding scenarios in training while minimizing injury risk. Full article

Aims: The present study evaluated the acute morning effect of melatonin supplementation (5 mg) on cardiometabolic responses. Methods: For this purpose, 12 physically active men (22.1 ± 1.3 years; 1.7 ± 01 m; 74.7 ± 12.1 kg; 24.3 ± 2.7 m/kg²; VO₂max: 46.9 ± 2.3 mL/kg/min; 17.3 ± 5.2%F) were measured in a double-blind crossover protocol, where participants were measured before, during, and after a high-intensity interval exercise (HIIE) protocol [4 × 4 min at 95% of maximum heart rate (HRmax) with a 3 min interval at 60–70% of HRmax] followed by 30 min of recovery. At rest, the following variables were measured: HR, systolic blood pressure (SBP), diastolic blood pressure (DBP), lactate, and maximum oxygen consumption (VO₂max). At the end of each stage and interval, VO₂, respiratory exchange ratio (RER), and HR were measured. During recovery, VO₂, VCO₂, RER, SBP, DBP, and HR were measured. Results: Melatonin significantly enhanced recovery metabolism, as evidenced by increased VO₂ at Interval 3 (+2.2 mL/kg/min, p = 0.03, d = 0.69) and 5 min postexercise (+2.4 mL/kg/min, p = 0.02, d = 0.81). The RER was higher during Sprint 4 (+0.08, p = 0.01, d = 0.84), indicating greater carbohydrate reliance. Cardiovascular recovery was also improved, with a reduced HR at 30 min (−5 bpm, p = 0.04, d = 0.66) and lower SBP at 15 min (−8 mmHg, p = 0.02, d = 0.75). Lactate concentration at 30 min was lower with melatonin (−0.7 mmol/L, p = 0.03, d = 0.72). No significant effects were observed at rest or during early exercise. Conclusions: Acute morning melatonin intake may amplify metabolic responses to HIIE while facilitating cardiometabolic recovery. This dual-phase action may benefit athletes aiming to optimize energy expenditure, fat metabolism, and recovery during early-day training. Full article

The concentration characteristics of SO₂, NO₂, O₃, and CO in the marine atmosphere are of great significance for understanding air–sea interactions and regional atmospheric chemical processes. However, due to the challenging conditions of marine monitoring, long-term continuous observational data remain scarce. To address this gap, this study proposes a Transfer Learning–Convolutional Neural Network (TL-CNN) model that integrates ERA5 meteorological data, EAC4 atmospheric composition reanalysis data, and ground-based observations through multi-source data fusion. During data preprocessing, the Data Interpolating Empirical Orthogonal Function (DINEOF), inverse distance weighting (IDW) spatial interpolation, and Gaussian filtering methods were employed to improve data continuity and consistency. Using ERA5 meteorological variables as inputs and EAC4 pollutant concentrations as training targets, a CNN-based inversion framework was constructed. Results show that the CNN model achieved an average coefficient of determination (R²) exceeding 0.80 on the pretraining test set, significantly outperforming random forest and deep neural networks, particularly in reproducing nearshore gradients and regional spatial distributions. After incorporating transfer learning and fine-tuning with station observations, the model inversion results reached an average R² of 0.72 against site measurements, effectively correcting systematic biases in the reanalysis data. Among the pollutants, the inversion of SO₂ performed relatively poorly, mainly because emission reduction trends from anthropogenic sources were not sufficiently represented in the reanalysis dataset. Overall, the TL-CNN model provides more accurate pollutant concentration fields for offshore regions with limited observations, offering strong support for marine atmospheric environment studies and assessments of marine ecological effects. It also demonstrates the potential of combining deep learning and transfer learning in atmospheric chemistry research. Full article

Diesel engines power much of the heavy-duty equipment used in underground mines, where exhaust emissions pose acute environmental and occupational health challenges. However, predicting the amount of air required to dilute these emissions is difficult because exhaust mass flow and pollutant concentrations vary nonlinearly with multiple operating parameters. We apply deep learning to predict the total exhaust mass flow and carbon monoxide (CO) concentration of a six-cylinder gas–diesel (dual-fuel) turbocharged KAMAZ 910.12-450 engine under controlled operating conditions. We trained artificial neural networks on the preprocessed experimental dataset to capture nonlinear relationships between engine inputs and exhaust responses. Model interpretation with Shapley additive explanations (SHAP) identifies torque, speed, and boost pressure as dominant drivers of exhaust mass flow, and catalyst pressure, EGR rate, and boost pressure as primary contributors to CO concentration. In addition, symbolic regression yields an interpretable analytical expression for exhaust mass flow, facilitating interpretation and potential integration into control. The results indicate that deep learning enables accurate and interpretable prediction of key exhaust parameters in dual-fuel engines, supporting emission assessment and mitigation strategies relevant to underground mining operations. These findings support future integration with ventilation models and real-time monitoring frameworks. Full article

Optimizing the neuromechanical determinants of explosive performance remains a key objective in sports science. This study compared the effects of an eight-week movement-pattern-based training program (MPT) with an isometric strength training protocol (ITG) on countermovement jump (CMJ) mechanics in competitive track and field athletes. Thirty-four athletes (19 men, 15 women) with ≥7 years of training experience were randomly allocated to the MPT or ITG. Pre- and post-intervention assessments were conducted using dual force plates to evaluate jump height, musculotendinous stiffness, concentric and eccentric impulses, contraction time, eccentric-to-concentric force ratio, and rate of force development (RFD). The MPT elicited significant gains in stiffness (Δ = +840.94 ± 1302.21 N/m; p = 0.002), maintained concentric peak force, and reduced contraction time (Δ = –64.53 ± 190.32 ms; p = 0.01), suggesting improved elastic efficiency and neuromuscular timing. Conversely, ITG was associated with reductions in concentric peak force (Δ = –66.18 ± 77.45 N; p = 0.003) and stiffness (Δ = –691.94 ± 1414.41 N/m) and an increase in the eccentric-to-concentric force ratio (Δ = +1.99%; p = 0.006). The RFD changes were inconsistent across both groups. These findings indicate that dynamic multi-joint training confers superior neuromechanical adaptations compared to isolated isometric loading. From a performance perspective, programming strategies should prioritize movement-specific dynamic tasks to enhance the explosive qualities critical for sprinting, jumping, and multidirectional field sports. Full article

Background: Sports vision training improves perceptual–motor skills crucial for performance and injury prevention. Despite proven benefits, little is known about its perception and use among coaches in Portugal. Methods: A cross-sectional online survey was completed by active coaches from various sports, gathering sociodemographic data, awareness of visual training, perceived importance of ten visual skills, and implementation in training plans. Statistical analyses included descriptive tests to summarize sample characteristics, t-tests and two-way ANOVA to compare perceived importance of visual skills across sex and sport modalities, Spearman correlations to assess associations with age, and Firth-corrected logistic regression to identify predictors of incorporating visual training into practice plans. Results: Among 155 participants (88.5% men; mean age 36.9 ± 11.8 years), 73.2% reported incorporating visual training, with no association with self-reported knowledge (p = 0.413). Regarding perceived importance, reaction time was rated highest (1.20 ± 0.44), followed by hand–eye/body coordination (1.61 ± 0.71) and anticipation (1.34 ± 0.55). Age negatively correlated with importance given to visual memory, peripheral vision, concentration, depth perception, coordination, and moving-object recognition (p < 0.05). Multivariable analysis showed age (OR = 1.05; p = 0.0206) and volleyball (OR = 2.45; p = 0.031) positively associated with implementation, while higher perceived importance for visual concentration was negatively associated (OR = 0.54; p = 0.0176). Conclusions: Visual training implementation is high but not always linked to formal knowledge. Adoption is influenced by sport and demographics, and the counterintuitive role of visual concentration underscores the need for tailored educational programs to enhance performance and reduce injury risk. Full article

The escalating prevalence of malware poses a significant threat to digital infrastructure, demanding robust yet efficient detection methods. In this study, we evaluate multiple Convolutional Neural Network (CNN) architectures, including basic CNN, LeNet, AlexNet, GoogLeNet, and DenseNet, on a dataset of 11,000 malware images spanning 452 families. Our experiments demonstrate that CNN models can achieve reliable classification performance across both multiclass and binary tasks. However, we also uncover a critical weakness in that even minimal image perturbations, such as pixel modification lower than 1% of the total image pixels, drastically degrade accuracy and reveal CNNs’ fragility in adversarial settings. A key contribution of this work is spatial analysis of malware images, revealing that discriminative features concentrate disproportionately in the bottom-left quadrant. This spatial bias likely reflects semantic structure, as malware payload information often resides near the end of binary files when rasterized. Notably, models trained in this region outperform those trained in other sections, underscoring the importance of spatial awareness in malware classification. Taken together, our results reveal that CNN-based malware classifiers are simultaneously effective and vulnerable to learning strong representations but sensitive to both subtle perturbations and positional bias. These findings highlight the need for future detection systems that integrate robustness to noise with resilience against spatial distortions to ensure reliability in real-world adversarial environments. Full article

The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques. This paper presents an intelligent three-step procedure for optimally designing earthquake-resistant buildings based on the training (1st step) and successive inversion (2nd step) of Multi-Layer Perceptron Neural Networks. This involves solving the inverse problem of determining the optimal design parameters that meet pre-assigned, code-based performance targets, by means of a gradient-based optimization algorithm (3rd step). The effectiveness of the procedure was tested using an archetypal multistory, moment-resisting, concentrically braced steel frame with active tension diagonal bracing. The input dataset was obtained by varying four design parameters. The output dataset resulted from performance variables obtained through non-linear dynamic analyses carried out under three earthquakes consistent with the Chilean code spectrum, for all cases considered. Three spectrum-consistent records are sufficient for code-based seismic design, while each seismic excitation provides a wealth of information about the behavior of the structure, highlighting potential issues. For optimization purposes, only information relevant to critical sections was used as a performance indicator. Thus, the dataset for training consisted of pairs of design parameter sets and their corresponding performance indicator sets. A dedicated MLP was trained for each of the outputs over the entire dataset, which greatly reduced the total complexity of the problem without compromising the effectiveness of the solution. Due to the comparatively low number of cases considered, the leave-one-out method was adopted, which made the validation process more rigorous than usual since each case acted once as a validation set. The trained network was then inverted to find the input design search domain, where a cost-effective gradient-based algorithm determined the optimal design parameters. The feasibility of the solution was tested through numerical analyses, which proved the effectiveness of the proposed artificial intelligence-aided optimal seismic design procedure. Although the proposed methodology was tested on an archetypal building, the significance of the results highlights the effectiveness of the three-step procedure in solving complex optimization problems. This paves the way for its use in the design optimization of different kinds of earthquake-resistant buildings. Full article

The purpose of this study was threefold: (a) to analyze differences in mean force, impulse, mean concentric and eccentric velocity, and peak concentric velocity across six repetitions of the flywheel deadlift exercise, with a particular focus on the first repetition initiated from zero momentum; (b) to explore relationships between these kinetic and kinematic variables and one-repetition maximum (1-RM) performance in the free-weight deadlift; (c) to examine the effects of different flywheel inertial loads on the relationships among mean force (MF), impulse, time under tension (TUT), and velocity, with the aim of identifying the most valid and reliable parameter for flywheel load prescription. Thirteen resistance-trained men (24.7 ± 5.0 y; 82.2 ± 11.7 kg; 1-RM deadlift: 174 ± 24 kg) performed six repetitions of the flywheel deadlift against six inertial loads (0.025 to 0.145 kg∙m²) on a kBox 5 device. Results showed that although the first repetition had 25–30% lower mean concentric velocity and 7–11% lower mean force compared to subsequent repetitions (p < 0.001), it exhibited 4–8% higher impulse due to the 14–20% longer time under tension. MF, velocity, and impulse in the first repetition showed moderate-to-strong correlations with 1-RM (r = 0.58 to 0.85, p < 0.05), particularly at the two higher inertia loads. MF plateaued at moderate inertia loads, while impulse and TUT increased linearly with increasing inertial load and demonstrated the strongest and most consistent relationships with inertial load (r = 0.99 ± 0.01 and 0.97 ± 0.02, p < 0.001), enabling individualized flywheel training prescription. This study highlights the distinct value of the first repetition in flywheel deadlifts and its practical value for both assessment and training. Also, it suggests that impulse and TUT may be used as simple and practical flywheel exercise prescription variables. Full article