Search Results (6,450)

This study investigates the effect of hemp shive granulometry on the thermal conductivity and microstructure of hemp–lime composites. Three distinct particle size fractions—fine, medium, and coarse—were characterized using high-resolution image analysis to determine geometric parameters such as Feret diameters, circularity, and elongation. Composite mixtures with varying binder-to-shive and water-to-shive ratios were prepared and compacted at a consistent level to isolate the influence of aggregate granulometry on thermal performance. Results demonstrate a clear inverse relationship between particle size and thermal conductivity, with coarse fractions reducing thermal conductivity by up to 7.6% compared to fine fractions. Composite density was also affected, decreasing with increasing particle size, confirming the impact of granulometry on pore structure and packing density. However, binder content exhibited the most significant effect on thermal conductivity, with a 20% increase observed for higher binder-to-shive ratios irrespective of shive size. The study further establishes that a 15 g sample size (~2400 particles) provides sufficient statistical accuracy for granulometric characterization using image analysis. These findings provide critical insights for optimizing hemp–lime composites for enhanced thermal insulation performance, supporting sustainable construction practices by informing material formulation and processing parameters. Full article

Digital twin technology is emerging as a core technology that models physical objects or systems in a digital space and links real-time data to accurately reflect the state and behavior of the real world. For the effective operation of such digital twins, high-performance visualization methods that support an intuitive understanding of the vast amounts of data collected from sensors and enable rapid decision-making are essential. The proposed system is designed as a balanced 3D monitoring solution that prioritizes intuitive, real-time state observation. Conventional 3D-simulation-based systems, while offering high physical fidelity, are often unsuitable for real-time monitoring due to their significant computational cost. Conversely, 2D-based systems are useful for detailed analysis but struggle to provide an intuitive, holistic understanding of multiple assets within a spatial context. This study introduces a visualization approach that bridges this gap. By leveraging sensor data, our method generates a physically plausible representation 3D CAD models, enabling at-a-glance comprehension in a visual format reminiscent of simulation analysis, without claiming equivalent physical accuracy. The proposed method includes GPU-accelerated interpolation, the user-selectable application of geodesic and Euclidean distance calculations, the automatic resolution of CAD model connectivity issues, the integration of Physically Based Rendering (PBR), and enhanced data interpretability through ramp shading. The proposed system was implemented in the Unity3D environment. Through various experiments, it was confirmed that the system maintained high real-time performance, achieving tens to hundreds of Frames Per Second (FPS), even with complex 3D models and numerous sensor data. Moreover, the application of geodesic distance yielded a more intuitive representation of surface-based phenomena, while PBR integration significantly enhanced visual realism, thereby enabling the more effective analysis and utilization of sensor data in digital twin environments. Full article

Plant secondary metabolites regulate plant growth and serve as valuable pharmaceutical resources. Napier grass (Pennisetum purpureum Schumach.), a Poaceae species, shows potential as a functional food. In this study, we employed high-resolution mass spectrometry combined with a data-independent acquisition (DIA) strategy for the untargeted detection of anthocyanins, a group of secondary metabolites, in napier grass. Clear MS² fragmentation patterns were observed for anthocyanins, characterized by diagnostic aglycone signals and sequential losses of hexosyl (C₆H₁₀O₅), deoxyhexosyl (C₆H₁₀O₄), pentosyl (C₅H₈O₄), and p-coumaroyl groups (C₉H₈O₃). Based on matching with authentic standards and an in-house database, ten anthocyanins were identified, seven of which were newly reported in napier grass. In a single-laboratory validation analysis, both absolute and semi-quantitative results reliably reflected the specific distribution of metabolites across different cultivars and plant organs. The purple cultivar (TS5) exhibited the highest anthocyanin content, with the cyanidin 3-O-glucoside content reaching 5.0 ± 0.5 mg/g, whereas the green cultivar (TS2), despite its less pigmented appearance, contained substantial amounts of malvidin 3-O-arabinoside (0.7 ± <0.1 mg/g). Flavonoid profiling revealed that monoglycosylated anthocyanins were the dominant forms in floral tissues. These findings shed light on napier grass metabolism and support future Poaceae breeding and functional food development. Full article

Mesoscale coastal eddies are key components of ocean circulation, mediating the transport of heat, nutrients, and marine debris. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution sea surface height data, offering a novel opportunity to improve the observation and characterization of these features, especially in coastal regions where conventional altimetry is limited. In this study, we investigate a mesoscale anticyclonic coastal eddy observed southwest of Mallorca Island, in the Balearic Sea, to assess the impact of SWOT-enhanced altimetry in resolving its structure and dynamics. Initial eddy identification is performed using satellite ocean color imagery, followed by a qualitative and quantitative comparison of multiple altimetric datasets, ranging from conventional nadir altimetry to wide-swath products derived from SWOT. We analyze multiple altimetric variables—Sea Level Anomaly, Absolute Dynamic Topography, Velocity Magnitude, Eddy Kinetic Energy, and Relative Vorticity—highlighting substantial differences in spatial detail and intensity. Our results show that SWOT-enhanced observations significantly improve the spatial characterization and dynamical depiction of the eddy. Furthermore, Lagrangian transport simulations reveal how altimetric resolution influences modeled transport pathways and retention patterns. These findings underline the critical role of SWOT in advancing the monitoring of coastal mesoscale processes and improving our ability to model oceanic transport mechanisms. Full article

Background/Objectives: The emergence of cathinone-based psychostimulants necessitates ongoing research and analysis of the characteristics and properties of novel derivatives. The metabolic fate of five novel cathinone-derived substances (ASProp, MASProp, MASPent, PySProp, and PySPent) containing a selenophene moiety was investigated in vitro and in vivo. Methods: All compounds were incubated individually with pooled human liver S9 fraction. A monooxygenase activity screening investigating the metabolic contribution of eleven recombinant phase I isoenzymes was conducted. Rat urine after oral administration was prepared by urine precipitation. Liquid chromatography–high-resolution tandem mass spectrometry was used for the analysis of all samples. Reversed-phase liquid chromatography (RPLC) and zwitterionic hydrophilic interaction liquid chromatography (HILIC) were used to evaluate and compare the metabolites’ chromatographic resolution. Results: Phase I reactions of ASProp, MASProp, MASPent, PySProp, and PySPent included N-dealkylation, hydroxylation, reduction, and combinations thereof. The monooxygenase activity screening revealed the contribution of various isozymes. Phase II reactions detected in vivo included N-acetylation and glucuronidation. Both chromatographic columns complemented each other. Conclusions: All substances revealed metabolic reactions comparable to those observed for other synthetic cathinones. Contributions from isozymes to their metabolism minimized the risk of drug–drug interactions. The identified metabolites should be considered as targets in human biosamples, especially in urine screening procedures. RPLC and HILIC can both be recommended for this purpose. Full article

Precise monitoring of vegetative growth is essential for assessing crop responses to environmental changes. Conventional methods of geometric characterization of plants such as RGB imaging, multispectral sensing, and manual measurements often lack precision or scalability for growth monitoring of rice. LiDAR offers high-resolution, non-destructive 3D canopy characterization, yet applications in rice cultivation across different growth stages remain underexplored, while LiDAR has shown success in other crops such as vineyards. This study addresses that gap by using LiDAR for geometric characterization of rice plants at early, middle, and late growth stages. The objective of this study was to characterize rice plant geometry such as plant height, canopy volume, row distance, and plant spacing using the proximal LiDAR sensing technique at three different growth stages. A commercial LiDAR sensor (model: VPL−16, Velodyne Lidar, San Jose, CA, USA) mounted on a wheeled aluminum frame for data collection, preprocessing, visualization, and geometric feature characterization using a commercial software solution, Python (version 3.11.5), and a custom algorithm. Manual measurements compared with the LiDAR 3D point cloud data measurements, demonstrating high precision in estimating plant geometric characteristics. LiDAR-estimated plant height, canopy volume, row distance, and spacing were 0.5 ± 0.1 m, 0.7 ± 0.05 m³, 0.3 ± 0.00 m, and 0.2 ± 0.001 m at the early stage; 0.93 ± 0.13 m, 1.30 ± 0.12 m³, 0.32 ± 0.01 m, and 0.19 ± 0.01 m at the middle stage; and 0.99 ± 0.06 m, 1.25 ± 0.13 m³, 0.38 ± 0.03 m, and 0.10 ± 0.01 m at the late growth stage. These measurements closely matched manual observations across three stages. RMSE values ranged from 0.01 to 0.06 m and r² values ranged from 0.86 to 0.98 across parameters, confirming the high accuracy and reliability of proximal LiDAR sensing under field conditions. Although precision was achieved across growth stages, complex canopy structures under field conditions posed segmentation challenges. Further advances in point cloud filtering and classification are required to reliably capture such variability. Full article

The data descriptor introduces an open, high-resolution dataset of real-world bus operations in Astana, Kazakhstan, captured from GPS trajectories between July and September 2024. The data covers three high-frequency routes and have been processed into a GTFS format, enabling direct use with existing transit modeling tools. Unlike typical static GTFS feeds, this dataset provides empirically observed dwell times, run times, and travel times, offering a detailed snapshot of operational variability in urban bus systems. The dataset supports applications in machine learning–based travel time prediction, timetable optimization, and transit reliability analysis, especially in settings where live feeds are unavailable. By releasing this dataset publicly, we aim to promote transparent, data-driven transport research in emerging urban contexts. Full article

Modern aerospace engineering places increasing emphasis on materials that combine low weight with high mechanical performance. Fiber metal laminates (FMLs), which merge metal layers with fiber-reinforced composites, meet this demand by delivering improved fatigue resistance, impact tolerance, and environmental durability, often surpassing the performance of their constituents in demanding applications. Despite these advantages, inspecting such thin, layered structures remains a significant challenge, particularly when they are difficult or impossible to access. As with any new invention, they always come with challenges. This study examines the effectiveness of the fundamental anti-symmetric Lamb wave mode (A0) in detecting weak interfacial defects within Carall laminates, a type of hybrid fiber metal laminate (FML). Delamination detectability is analyzed in terms of strong wave dispersion observed downstream of the delaminated sublayer, within a region characterized by acoustic distortion. A three-dimensional finite element (FE) model is developed to simulate mode trapping and full-wavefield local displacement. The approach is validated by reproducing experimental results reported in prior studies, including the author’s own work. Results demonstrate that the A0 mode is sensitive to delamination; however, its lateral resolution depends on local position, ply orientation, and dispersion characteristics. Accurately resolving the depth and extent of delamination remains challenging due to the redistribution of peak amplitude in the frequency domain, likely caused by interference effects in the acoustically sensitive delaminated zone. Additionally, angular scattering analysis reveals a complex wave behavior, with most of the energy concentrated along the centerline, despite transmission losses at the metal-composite interfaces in the Carall laminate. The wave interaction with the leading and trailing edges of the delaminations is strongly influenced by the complex wave interference phenomenon and acoustic mismatched regions, leading to an increase in dispersion at the sublayers. Analytical dispersion calculations clarify how wave behavior influences the detectability and resolution of delaminations, though this resolution is constrained, being most effective for weak interfaces located closer to the surface. This study offers critical insights into how the fundamental anti-symmetric Lamb wave mode (A0) interacts with delaminations in highly attenuative, multilayered environments. It also highlights the challenges in resolving the spatial extent of damage in the long-wavelength limit. The findings support the practical application of A0 Lamb waves for structural health assessment of hybrid composites, enabling defect detection at inaccessible depths. Full article

Fused Filament Fabrication (FFF) enables the manufacturing of complex polymer components. However, surface finish and dimensional accuracy remain key limitations for their integration into functional assemblies. This study explores the potential of conventional turning as a post-processing strategy to improve the geometric and surface quality of PLA reinforced with carbon fiber (CF) parts produced by FFF. Machinability was evaluated through the analysis of cutting forces, thermal behavior, energy consumption, and surface integrity under varying cutting speeds, feed rates, and specimen slenderness. The results indicate that feed is the most influential parameter across all performance metrics, with lower values leading to improved dimensional accuracy and surface finish, achieving the most significant reductions of 63% in surface roughness (Sa) and 62% in cylindricity deviation. Nevertheless, the surface roughness is higher than that of metals, and deviations in geometry along the length of the specimen have been observed. A critical shear stress of 0.237 MPa has been identified as the limit for interlayer failure, defining the boundary conditions for viable cutting operation. The incorporation of CNC turning as a post-processing step reduced the total fabrication time by approximately 83% compared with high-resolution FFF, while maintaining dimensional accuracy and enhancing surface quality. These findings support the use of machining operations as a viable and efficient post-processing method for improving the functionality of polymer-based components produced by additive manufacturing. Full article

Accurate estimation of erupted lava volumes is essential for understanding volcanic processes, interpreting eruptive cycles, and assessing volcanic hazards. Traditional methods based on Mid-Infrared (MIR) satellite imagery require clear-sky conditions during eruptions and are prone to sensor saturation, limiting data availability. Here, we present an alternative approach based on the post-eruptive Thermal InfraRed (TIR) signal, using the recently proposed VRP_TIR method to quantify radiative energy loss during lava flow cooling. We identify thermally anomalous pixels in VIIRS I5 scenes (11.45 µm, 375 m resolution) using the TIRVolcH algorithm, this allowing the detection of subtle thermal anomalies throughout the cooling phase, and retrieve lava flow area by fitting theoretical cooling curves to observed VRP_TIR time series. Collating a dataset of 191 mafic eruptions that occurred between 2010 and 2025 at (i) Etna and Stromboli (Italy); (ii) Piton de la Fournaise (France); (iii) Bárðarbunga, Fagradalsfjall, and Sundhnúkagígar (Iceland); (iv) Kīlauea and Mauna Loa (United States); (v) Wolf, Fernandina, and Sierra Negra (Ecuador); (vi) Nyamuragira and Nyiragongo (DRC); (vii) Fogo (Cape Verde); and (viii) La Palma (Spain), we derive a new power-law equation describing mafic lava flow thickening as a function of time across five orders of magnitude (from 0.02 Mm³ to 5.5 km³). Finally, from knowledge of areas and episode durations, we estimate erupted volumes. The method is validated against 68 eruptions with known volumes, yielding high agreement (R² = 0.947; ρ = 0.96; MAPE = 28.60%), a negligible bias (MPE = −0.85%), and uncertainties within ±50%. Application to the February-March 2025 Etna eruption further corroborates the robustness of our workflow, from which we estimate a bulk erupted volume of 4.23 ± 2.12 × 10⁶ m³, in close agreement with preliminary estimates from independent data. Beyond volume estimation, we show that VRP_TIR cooling curves follow a consistent decay pattern that aligns with established theoretical thermal models, indicating a stable conductive regime during the cooling stage. This scale-invariant pattern suggests that crustal insulation and heat transfer across a solidifying boundary govern the thermal evolution of cooling basaltic flows. Full article

Numerical weather prediction of warm season rainfall remains challenging and skill at achieving this is often much lower than during the cold season. Prior studies have shown that displacement errors play a large role in the poor skill of these forecasts, but less is known about how such errors compare to other sources of error, particularly within forecasts from convection-allowing ensembles. The present study uses the Method for Object-based Diagnostic Evaluation to develop a climatology of errors for precipitation objects from High-Resolution Ensemble Forecasting forecasts for mesoscale convective systems during the warm seasons from 2018 to 2023 in the United States. It is found that displacement errors in all ensemble members are generally not systematic, and on average are between 100 and 150 km. Errors are somewhat smaller in September, possibly reflecting increased forcing from synoptic-scale systems. Although most ensemble members have a negative error for the 10th percentile of rainfall intensity, the error becomes positive for heavier amounts. However, the total system rainfall is less than that observed for all members except the 12 UTC NAM. This is likely due to the negative errors for area that are present in all models, except again in the 12 UTC NAM. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

The airborne Synthetic Aperture Radar (SAR) has the advantages of high-precision real-time observation of wave height variations and portability in the high frequency band, such as the Ku band. In view of the Four Fast Fourier Transform (4-FFT) algorithm, combined with a Gaussian operator, a Laplacian of Gaussian (LoG) Phase Unwrapping (PU) expression was derived. Then, an Adaptive LoG (ALoG) algorithm was proposed based on adaptive variance, further optimizing the algorithm through iteration. Building the models of Kelvin wake on the sea surface and height to phase, the interferometric phase of wave height can be simulated. These PU algorithms were qualitatively and quantitatively evaluated. The Principal Component Analysis (PCA) scores of the ALoG iteration (ALoGI) algorithm are the best under the tested noise levels of the simulation. Through a simulation experiment, it has been proven that the superiority of the ALoGI algorithm in high spatial resolution inversion for the sea-ship surface height of the Kelvin wake, with good stability and noise resistance. Full article

High-resolution groundwater storage is essential for effective regional water resource management. While Gravity Recovery and Climate Experiment (GRACE) satellite data offer global coverage, the coarse spatial resolution (0.25–0.5°) limits the data’s applicability at regional scales. Traditional downscaling methods often fail to effectively capture spatial heterogeneity within regions, leading to reduced model performance. To overcome this limitation, a zoned downscaling strategy based on time series clustering is proposed. A K-means clustering algorithm with dynamic time warping (DTW) distance, combined with a random forest (RF) model, was employed to partition the Hexi Corridor region into relatively homogeneous subregions for downscaling. Results demonstrated that this clustering strategy significantly enhanced downscaling model performance. Correlation coefficients rose from 0.10 without clustering to above 0.84 with K-means clustering and the RF model, while correlation with the groundwater monitoring well data improved from a mean of 0.47 to 0.54 in the first subregion (a) and from 0.40 to 0.45 in the second subregion (b). The driving factor analysis revealed notable differences in dominant factors between subregions. In the first subregion (a), potential evapotranspiration (PET) was found to be the primary driving factor, accounting for 33.70% of the variation. In the second subregion (b), the normalized difference vegetation index (NDVI) was the dominant factor, contributing 29.73% to the observed changes. These findings highlight the effectiveness of spatial clustering downscaling methods based on DTW distance, which can mitigate the effects of spatial heterogeneity and provide high-precision groundwater monitoring data at a 1 km spatial resolution, ultimately improving water resource management in arid regions. Full article

Single-image super-resolution refers to the reconstruction of a high-resolution image from a single low-resolution observation. Although recent deep learning-based methods have demonstrated notable success on simulated datasets—with low-resolution images obtained by degrading and downsampling high-resolution ones—they frequently fail to generalize to real-world settings, such as document scans, which are affected by complex degradations and semantic variability. In this study, we introduce a task-driven, multi-task learning framework for training a super-resolution network specifically optimized for optical character recognition tasks. We propose to incorporate auxiliary loss functions derived from high-level vision tasks, including text detection using the connectionist text proposal network (CTPN), text recognition via a convolutional recurrent neural network (CRNN), keypoints localization using Key.Net, and hue consistency. To balance these diverse objectives, we employ a dynamic weight averaging (DWA) mechanism, which adaptively adjusts the relative importance of each loss term based on its convergence behavior. Experimental evaluation demonstrates that the proposed approach improves text detection, measured with intersection over union, by 1.09% for simulated and 1.94% for real-world datasets containing scanned documents, while preserving overall image fidelity. These improvements are statistically significant as confirmed by the Kruskal–Wallis H test and the post hoc Dunn test with Benjamini–Hochberg p-value correction. Our findings highlight the value of multi-objective optimization in super-resolution models for bridging the gap between simulated training regimes and practical deployment in real-world scenarios. Full article