Search Results (898)

Objective: This study aims to explore the differences in chemical composition among Tibetan medicinal Rhododendron species and their potential correlation with anti-complement activity, with the goal of identifying promising medicinal resources. In Tibetan medicinal practice, the two groups of large-leaved Rhododendron (Tibetan: Dama) and small-leaved Rhododendron (Tibetan: Tali) are often used interchangeably despite unclear chemical and taxonomic bases. By comparing chemical profiles and evaluating anti-complement effects, this investigation seeks to provide preliminary scientific evidence for clarifying medicinal origins and facilitating the targeted development of high-quality resources. Methods: Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was employed to analyze seven Rhododendron samples. Separation was achieved on a Waters CORTECS UPLC C18 column (2.1 × 100 mm, 1.6 μm) using a gradient mobile phase system consisting of acetonitrile and 0.1% formic acid in water, at a flow rate of 0.3 mL/min and a column temperature of 30 °C. Data were acquired in both positive and negative electrospray ionization (ESI) modes. Compound identification was performed using Peakview 1.2 software by comparison with databases and literature. Grey relational analysis and partial least squares (PLS) regression, combined with 5000 bootstrap resampling iterations, were applied to establish spectrum–effect relationships and to screen for characteristic peaks potentially associated with anti-complement activity. Results: A total of 52 compounds were tentatively identified, including flavonoids (e.g., hyperin, isoquercitrin, taxifolin-3-O-arabinoside), terpenoids (e.g., grayanotoxin I/III), and chromanes (e.g., anthopogochromane series). The CH₅₀ values of the ethanol extracts ranged from 179.29 to 579.47 μg/mL, with Rhododendron principis showing the strongest activity (179.29 ± 11.86 μg/mL), followed by Rhododendron vellereum (198.61 ± 7.93 μg/mL). Spectrum–effect analysis revealed that four unidentified peaks (F5315, F5822, F5368, F5991) exhibited negative regression coefficients and VIP means close to or above 0.8, suggesting a possible positive correlation with anti-complement activity. Among these, F5315 (VIP = 0.909), F5822 (VIP = 0.877), and F5368 (VIP = 0.834) showed relatively higher values and were considered preliminary candidate peaks warranting further investigation. Conclusions: This study tentatively identifies 52 compounds from the ethanol extracts of seven Tibetan medicinal Rhododendron species and reports their anti-complement activities. The findings reveal chemical distinctions between the large-leaved (Dama) and small-leaved (Tali) groups, offering a potential chemical basis for species differentiation and quality evaluation. Furthermore, four unknown peaks were preliminarily screened through spectrum–effect analysis as potential anti-complement candidates, which may serve as a foundation for future activity-guided isolation and quality marker studies. Full article

Countermovement jump (CMJ) analysis is widely used to assess lower limb neuromuscular function, but commercial force plates often suffer from high cost, closed algorithms, and lack of bilateral independent measurement. This study developed and evaluated a dual channel strain gauge force plate system featuring open architecture and hardware-triggered video synchronization. The system consists of two physically isolated plates, each with four full bridge strain beams, a precision analog front end, and a 2000 Hz acquisition unit. A microcontroller-based hardware trigger synchronizes force data with video capture. Custom host software implements adaptive jump phase recognition and calculates peak force (PF), concentric impulse, jump height, rate of force development (RFD), and asymmetry index (ASI). Validation included static mass measurements in 14 participants, low-load static calibration (5.0–30.0 kg), free-fall impulse validation (7.00 to 31.32 N·s), 240 fps high-speed video cross validation of flight time, ecological-validity comparison with published AMTI-based force-plate data, and 48 h test–retest reliability assessment. Static mass measurement showed a mean absolute percentage error (MAPE) of 1.01% and a coefficient of determination (R²) of 0.9992, while low-load testing confirmed excellent linearity (R² > 0.996) and minimal absolute error (mean absolute error = 0.34 kg) at lighter weights. Dynamic impulse validation yielded R² > 0.997 and MAPE < 3%. Flight time agreement with high-speed video was within ±10 ms. Test–retest reliability was excellent for concentric impulse (intraclass correlation coefficient (ICC) = 0.997) and jump height (ICC = 0.987), and good for PF (ICC = 0.962) and rate of force development at 100 ms (RFD_100ms) (ICC = 0.883). The physically isolated dual-plate architecture effectively captured bilateral force differences, although the ASI demonstrated moderate reliability (ICC = 0.748), likely reflecting the inherent biological variability in bilateral coordination. The ecological-validity comparison further indicated that the macroscopic kinetic outputs of the proposed system fell within the expected physiological and biomechanical ranges reported for adult CMJ testing. Overall, these findings support the study hypothesis that the proposed dual-channel force plate system provides a valid, reliable, and cost-effective solution for synchronized bilateral CMJ kinetic assessment in sports performance monitoring and biomechanical research, while offering improved accessibility through an open-source and transparent analysis framework with a hardware cost below 500 USD. Full article

Herbicide overuse remains a major challenge in sugar beet production because of its environmental and economic impacts. This study addresses three key gaps in UAV-based weed mapping: the lack of leakage-aware benchmarks for North African sugar beet imagery, the limited controlled comparison of one-stage and two-stage detectors under identical experimental conditions, and the limited translation of detection outputs into decision-support layers for site-specific spraying. We develop a reproducible UAV-based deep learning pipeline and present a field case study from Beni Mellal, Morocco. Fast R-CNN, YOLOR, YOLOv7, and YOLOv5 were compared under a unified protocol using identical data partitions, input resolution, augmentation strategies, and evaluation metrics, with locally acquired RGB imagery, COCO-format annotations, and leakage-aware field/flight splits. Under the tested conditions, YOLOv5 achieved the strongest performance, with 97.82% precision, 83.05% recall, 91.61% mAP@0.5, and 72.63% mAP@0.5:0.95. Error analysis indicated that missed detections were mainly associated with small weeds, partial occlusion by sugar beet leaves, and visually similar broadleaf weeds. Detector outputs were further organized into weed-intensity maps and used in a pilot scan-guided spot-treatment workflow on the surveyed parcels. This pilot implementation demonstrates the feasibility of translating UAV detections into prescription layers, but it should not be interpreted as a complete multi-season agronomic or economic validation. The main contribution is therefore a leakage-aware, unified benchmarking protocol and a reproducible end-to-end workflow from UAV detections to field-ready prescription maps. Future work should quantify herbicide savings, treatment efficacy, yield response, economic return, edge-device throughput, and transferability across regions and seasons. Full article

Advances in electric flight technologies have enabled distributed electric propulsion, opening a large design space for electric vertical take-off and landing (eVTOL) aircraft with diverse configurations and mission profiles. To support rapid exploration of these trade-offs, a computationally efficient sizing and performance evaluation tool has been developed. This study focuses on the verification of the key methods within the framework. The propeller sizing and performance model is verified against conventional helicopter rotors and representative eVTOL designs, while the battery discharge model is assessed using experimental data. In addition, the overall aircraft sizing is evaluated for two configurations of NASA’s Urban Air Mobility reference vehicles and compared with results obtained using NASA’s state-of-the-art rotorcraft design tool NDARC. The results show good agreement across all levels of verification. Average deviations are within 8% for propeller performance, below 5% for battery discharge, and within 4% for maximum take-off and empty mass. Mission performance and energy consumption are predicted within approximately 10%, demonstrating the suitability of the methodology for early-stage eVTOL design. Full article

Digital Elevation Models (DEMs) provide essential information for decision-making in precision agriculture. This study evaluated the altimetric quality of DEMs generated by Remotely Piloted Aircraft (RPA) platforms, the influence of flight direction, and the effect of mechanically disturbed soil surface conditions. We obtained data from a 900 m² area. Flights were conducted under pre- and post-mechanization conditions using a reversible plow, with flights in both longitudinal and transverse directions. We processed images using Structure-from-Motion (SfM) techniques to generate dense point clouds and DEMs. Statistical analyses relied on raster statistics and elevation cross-section transects of microtopography, were evaluated via descriptive statistics, ANOVA, Tukey’s HSD tests, and spatialization with micro-variation classification. Significant differences emerged among the evaluated models (p < 0.001), with Phantom-derived DEMs showing systematically higher elevations than Mavic models (617.31 ± 0.16 m vs. 605.41 ± 0.23 m, respectively). Post-plowing longitudinal flights showed the least variation, indicating greater altimetric consistency after secondary soil preparation. Conversely, the pre-plowing transverse flight (Mavic Flight 2) produced the largest errors. Quantitative assessment of topographic profiles revealed high morphological correspondence between platforms, with Pearson correlation coefficients ranging from 0.84 to 0.96 after vertical normalization, confirming that terrain morphology was preserved despite systematic vertical offsets. The effect of flight direction was more pronounced before soil preparation; after harrowing (a homogeneous surface), the difference between directions decreased, but longitudinal flights maintained an advantage, while transverse flights (especially Mavic) tended to overestimate elevations spatially. Full article

Racing pigeons (Columba livia domestica) have been selectively bred for centuries for superior flight capacity. Yet, the quantitative structure of flight performance traits and the extent to which sex influences these parameters remain poorly characterized, particularly in Turkish populations. This study aimed to evaluate flight performance in racing pigeons raised in the South Marmara region of Türkiye using three key kinematic traits (flight duration, speed, and distance) and to explore the multivariate structure and individual variation of these parameters through an integrative machine learning framework. Data were compiled from 166 individually registered pigeons (77 females, 89 males), totaling 781 race records used for pattern analysis. A composite Flight Performance Score (FPS) was constructed using min–max normalized component variables, and its internal consistency was assessed via Cronbach’s alpha and principal component analysis. Univariate comparisons revealed no statistically significant sex-related differences in any of the three flight parameters (p > 0.05 for all traits). Principal component analysis confirmed substantial overlap between male and female individuals in multivariate trait space, and Random Forest classification failed to discriminate between sexes above chance level (accuracy = 0.490; ROC-AUC = 0.500), collectively indicating that sex is not a dominant determinant of flight performance in this population. Internal consistency analysis revealed that flight duration, speed, and distance are functionally independent dimensions (Cronbach’s α = 0.135; r = −0.749 between duration and speed), with PCA of the FPS component variables indicating an effectively two-dimensional variance structure (PC1: 60.1%; PC2: 39.7%). Pattern analysis of race records identified four biologically distinct flight performance profiles, characterized by differential trade-offs among flight duration, speed, and distance, suggesting that individual-level performance strategy, rather than sex, is the primary axis of variation in this dataset. These findings challenge common breeder assumptions about sex-based differences in performance and highlight the multidimensional, individual-specific nature of flight performance in racing pigeons. Full article

Air Traffic Control Officers (ATCOs) are responsible for controlling air traffic and ensuring the safety of the aircraft. Capacity, understood as the maximum number of aircraft that can be safely managed for one hour, is calculated based on the workload of ATCOs. This calculation normally is based on a manual and tedious data collection process that demands a high consumption of human resources. To improve and relieve human re-sources, automation tools that automatically generate a preliminary annotation of Air Traffic Control (ATC) activity have been developed. This paper focuses on the feasibility of employing data-driven approaches using neural networks to classify ATC events, as well as if it is possible to improve the performance of these ATC-activity tools. Particularly, this approach seeks to infer ATC intent for separation actions, which are the most critical in terms of ATC workload. A modular methodology has been developed to include information from different sources: flight plans, radar trajectories, trajectory prediction, conflict detection and rule-based knowledge. Different experiments are evaluated based on the different input’s combination, as well as three neural networks (Multilayer Perceptron, Convolutional Neural Network and TabNet). Results show that TabNet is the best neural network option, reaching a similar performance in task classification than current ATC tools and improving classification metrics around 4% by employing the outputs of ATC tool metrics as inputs. Full article

The Vortex Ring State (VRS) poses a catastrophic aerodynamic threat to coaxial dual-rotor unmanned aerial vehicles (UAVs). Traditional reactive detection mechanisms provide insufficient altitude for recovery, while existing data-driven diagnostics are severely bottlenecked by data leakage, extreme class imbalance, and a lack of physical interpretability. To bridge these gaps, this paper proposes a physics-informed multimodal deep learning framework that transitions from post-occurrence detection to proactive early warning. We establish a 1.5 s precursor window—creating a three-class ordinal state space—to provide the flight control system with critical intervention time for differential rotor recovery. We developed a novel ViT-LSTM architecture (MTSF-Net) to fuse continuous seven-channel onboard-recorded data (comprising three-axis acceleration, three-axis angular velocity, and barometric vertical velocity), which are subsequently transformed into Continuous Wavelet Transform (CWT) spectrograms. To ensure real-time unidirectional inference while preserving absolute physical vibration scales across heterogeneous sensors, a Calibrated Benchmark Normalization (CBN) strategy is introduced. Furthermore, a Hybrid Ordinal Loss is proposed to mitigate the extreme sample imbalance (<0.5%) of the precursor state by penalizing asymmetric aerodynamic degradation. Evaluated under a strict sortie-based isolation protocol, the proposed system achieves an exceptional test accuracy of 98.26% and an unprecedented precursor recall of 100%. Notably, it completely eliminates fatal missed detections (VRS predicted as Normal) and false-positive VRS predictions triggered by precursor states. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to verify that the multimodal sensor processing pipeline successfully anchors onto authentic physical vibration frequencies rather than artifactual noise, laying a rigorous, interpretable foundation for intelligent aviation safety systems. Full article

Background: Recent technology improvements have enabled desorption electrospray ionisation (DESI) mass spectrometry imaging to achieve down to 5 µm (pixel) image resolution. However, operating at this resolution introduces challenges, particularly regarding increased total analysis time and the need for sufficient instrument sensitivity to detect analytes from very small tissue areas. Methods: High mass and image resolution DESI imaging was performed on rat brain tissue using a Xevo™ MRT benchtop mass spectrometer equipped with a multi-reflecting time-of-flight mass analyser and a DESI XS source. Data acquisition was conducted at speeds of up to 100 Hz. Sensitivity was assessed using a dilution series of five Active Pharmaceutical Ingredients (APIs) spotted onto porcine liver tissue. Signal detection limits were evaluated using extracted ion chromatograms (XICs) with signal-to-noise (S/N) calculations against blank samples. Additionally, enhanced duty cycle (EDC) was applied to evaluate improvements in analyte signal intensity across specific mass ranges in both positive and negative ionisation modes. Results: At acquisition speeds of up to 100 Hz, excellent data quality was achieved, with signal intensity remaining suitable for analytical applications. All five tested APIs were detectable at concentrations of 25 pg/mm². Three of the five compounds were further detected at concentrations as low as 2.5 pg/mm², with signal-to-noise ratios greater than 5. The application of EDC resulted in a significant increase in analyte signal intensity within the targeted mass ranges, particularly for small molecule endogenous metabolites and lipids, in both ionisation modes. Furthermore, the system demonstrated substantially improved spectral quality, achieving mass resolution up to 100,000 FWHM. This enabled the resolution of previously indistinguishable analytes with significantly improved mass accuracy compared to systems operating at approximately 30,000 FWHM. Conclusions: The Xevo™ MRT mass spectrometer with DESI XS source enables high-resolution DESI imaging at speeds up to 100 Hz without compromising data quality or sensitivity. The system demonstrates excellent detection limits for pharmaceutical compounds and improved performance through enhanced duty cycle operation. Overall, the combination of high spatial resolution, increased mass resolution, and improved spectral quality allows for more accurate analyte differentiation, representing a significant advancement over lower-resolution systems. Full article

Accurately obtaining aircraft aerodynamic parameters is essential for improving flight performance, optimizing design and control strategies, and ensuring flight safety. In this study, the improved Northern Goshawk Optimization (SPNGO) algorithm is used to optimize the kernel parameters and regularization coefficients of the Kernel Extreme Learning Machine (KELM). To address the defects of the original NGO algorithm, such as insufficient global optimization ability and being prone to falling into local optimums, two improvement strategies are proposed. The enhanced SPNGO algorithm is verified by 14 benchmark test functions, and the proposed SPNGO-KELM model is evaluated using open-source F-16 nonlinear simulation data for longitudinal aerodynamic parameter identification. The results demonstrate its effectiveness under the considered simulation conditions, while further validation with real flight-test data is required before application to actual flight environments. Comparative analysis with KELM, NGO-KELM, SSA-KELM, and WOA-KELM models shows that a single KELM is difficult to achieve high-precision aerodynamic parameter identification, and other comparison models have obvious fitting deviations in non-steady-state and strong nonlinear regions. Notably, the SPNGO-KELM model achieves the best identification performance, with a determination coefficient (R²) of 0.96537 and a mean absolute percentage error (MAPE) as low as 3.1574%. Its comprehensive identification accuracy is 1.81% to 37.98% higher than that of the comparison models, and it can effectively suppress error oscillations in nonlinear regions. Experimental results show that the proposed algorithm has excellent identification accuracy, generalization ability, and anti-interference performance. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment. By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

The sortie generation rate (SGR), a key metric of carrier-based aircraft operations, is severely degraded by multi-source coupled faults across the human–equipment–environment triad. Existing models oversimplify these dynamics by employing static failure probabilities and treating contributing factors in isolation, thereby underestimating systemic risk. To address this, we propose a mechanism-driven, hybrid simulation framework that dynamically captures fault coupling and cascading effects within the phased-mission system (PMS) of flight deck operations. First, 22 basic fault events are identified via fuzzy fault tree analysis (FFTA) and translated into a Bayesian network (BN) to establish a probabilistic baseline. A multi-source coupled fault model is then constructed, integrating human reliability, time-varying equipment degradation, and fault stress propagation to describe spatiotemporal coupling. A protocol is designed to robustly simulate heterogeneous fault dynamics within a discrete-continuous hybrid engine. Simulation experiments demonstrate that: (1) the baseline replicates real-world exercise data, validating framework credibility; (2) the model reveals a nonlinear SGR degradation with a sharp decline beyond a critical maintenance-pressure threshold, a behavior missed by static models; and (3) a comprehensive maintenance strategy improves long-term SGR by 73.13% over a reactive baseline. This framework provides a scalable testbed for evaluating operational resilience and informing maintenance strategies for next-generation aircraft carriers. Full article

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have become valuable tools in precision agriculture, enabling the monitoring of crop health, biomass estimation, and stress detection. However, the effectiveness of these measurements depends on several factors, including repeatability, sensitivity, and accuracy. Understanding these factors is crucial to ensure reliable data collection, particularly in regions with fluctuating weather patterns. This study evaluated the sensitivity of multispectral data collected within a short time frame and its impact on vegetation indices in normal field conditions. Measurements were taken over three days, with three UAV flights performed each day. Multispectral data were analyzed to identify statistically significant differences in vegetation indices, with calculations performed independently for each measurement day. The repeatability of vegetation indices varied between measurement days. When all measurement days were analyzed together, GARI, GNDVI, NDRE, and NDVI were the only indices that did not show statistically significant differences between flights. However, the magnitude of differences varied depending on the index, with some indices showing only minor variations between flights. Full article

The European spruce bark beetle (Ips typographus) is the main pest of spruce in northwestern European Russia, particularly in the Leningrad Region. Its outbreaks occur quite frequently. However, the population dynamics of Ips typographus in this region remain poorly understood. The aim of this study is to identify the life cycle characteristics of the species based on data obtained using pheromone traps in the Leningrad Region, to clarify the influence of various factors, and to evaluate the effectiveness of this monitoring method. From 2022 to 2025, observations using barrier pheromone traps north and south of St. Petersburg at several points were carried out. There were 3 traps placed at each point. The traps were inspected at 5-day intervals from early May to late August. The dynamics of beetle flight was obtained based on standardized values of beetle catches. The relationship between beetle swarming and temperature was estimated based on calculated Growing Degree-Days for 2022, 2023 and 2025. A graphical representation of 5-day moving average for daily temperature, relative humidity, wind speed and atmospheric pressure in accordance with calculated swarming dynamics were illustrated. The spring mass flight of the parent generation correlated strongly with daily temperature, but no significant correlation was found with other factors or outside the spring period. Catches after spring flight did not reflect actual population levels. Pheromone traps reliably reflect population density only during the spring flight of the parent generation. Full article

A biomechanical model of the flight phase in the long jump was constructed to analyze the factors influencing performance. Time-series coordinate data of key joints, obtained through AI-based human pose estimation, were incorporated into the model. For Problem 1, vertical velocity and acceleration of the joints were calculated using the dynamic parameter framework, and reference values with adaptive thresholds were applied to precisely identify take-off and landing moments. For Problem 2, joint information was used to derive arm swing amplitude, take-off angle, and joint rate of change as metrics to characterize athletes’ movement patterns and enable comparison before and after training. For Problem 3, physical and kinematic features were integrated, and Random Forest, multiple linear regression, and Recursive Feature Elimination were employed to evaluate key determinants of long jump performance and provide targeted training recommendations. The first two models achieved R² of 0.9772 and 0.9526, respectively, indicating excellent predictive accuracy. Finally, for Problem 4, the Random Forest and regression models developed in Problem 3 were applied to predict the performance of an athlete following posture optimization training. Full article