Search Results (518)

Urban coastal areas along the Mediterranean are exposed to short-duration convective rainfall, producing infrastructure disruptions and flood-related impacts. This study analyzes 45 rainfall episodes in the Metropolitan Area of Barcelona between 2014 and 2022, combining radar products, rain gauge observations, and urban-scale impact datasets. Storm radar tracking enabled the identification of key spatiotemporal features and assessment of short-term forecasting performance. Convective cells were typically short-lived, lasting less than 30 min in most cases. The main goal of the research has been the comparison between VIL density (DVIL) radar field and short-duration rainfall intensity provided by rain gauges. This is the first study comparing both data types, being a pioneer in this field. We have found a linear relationship between both data types, with weaker values for larger values. More persistent cells had higher DVIL values, observing a difference in behavior with a break point at 2 g/m³. The tracking and nowcasting system were evaluated based on its ability to anticipate convective precipitation. It achieved good scores values (POD of 0.73 and FAR of 0.33), considering the difficulties of tracking this type of convective system. Finally, false alarms associated with elevated DVIL values suggested the difficulty of capturing storm severity by surface-based precipitation measurements. Full article

We developed a robust tri-platform co-simulation framework that integrates Simulink, AMESim, and RecurDyn to address the dynamic inconsistencies observed in traditional tensioning models for tracked vehicles. The proposed framework synchronizes nonlinear hydraulic dynamics, closed-loop control, and track–ground interactions within a unified time step, thereby ensuring causal consistency along the pressure–flow–force–displacement power chain. Five representative operating conditions—including steady tension tracking, random road excitation, steering/braking pulses, supply-pressure drops, and parameter perturbations—were analyzed. The results show that the tri-platform model reduces tracking error by up to 60%, shortens recovery time by 35%, and decreases energy consumption by 12–17% compared with dual-platform models. Both simulations and full-scale experiments confirm that strong cross-domain coupling enhances system stability, robustness, and energy consistency under variable supply pressure and parameter uncertainties. The framework provides a high-fidelity validation tool and a transferable modeling paradigm for electro-hydraulic actuation systems in tracked vehicles and other multi-domain machinery. Full article

Drone photogrammetry and per-step spatial analysis were used to re-evaluate the West Gold Hill Dinosaur Tracksite (Bluff Sandstone, Colorado), which preserves an exceptionally long sauropod pes trackway. Building on earlier segment-based descriptions, we reconstructed the entire succession at millimetre-level resolution and quantified turning and gait asymmetry within an integrated digital workflow (UAV photogrammetry, Blender-based landmarking, scripted analysis). Of 134 footprints previously reported, 131 were confidently identified along a mapped path of 95.489 m that records 340° cumulative anticlockwise reorientation. Traditional end-point tortuosity (direct distance/trackway length; DL/TL) yields a moderate ratio of 0.462, whereas our incremental analysis isolates a fully looped subsection (tracks 38–83) with tortuosity of 0.0001 (DL 0.005 m; TL 34.825 m), revealing extreme local curvature that global (end-to-end) measures dilute. Gauge varies substantially along the trackway: the traditional metric (single pes width) averages 32.2% (wide gauge) with numerous medium-gauge representatives, while footprint-specific (‘incremental’) gauge spans 23.1–71.0% (narrow/medium/wide gauges observed within the same trackway). Our tests for asymmetry quantified that left-to-right paces and steps are longer (p = 0.001 and 0.008, respectively), central trackway width is greater (p = 0.043), and pace angulation is lower (p = 0.040) than right-to-left. Behaviourally, these signals are consistent with right-side load-avoidance but remain speculative (alternative explanations may include habitual laterality, local substrate heterogeneity). The study demonstrates how UAV-enabled, fully digital, sequential analyses can recover intra-trackway variability and enhance behavioural understanding of extinct trackmakers from fossil trackways. Full article

To enhance the precision, load capacity, disturbance rejection, and reliability of shipborne parallel stabilization platforms under complex sea conditions, this paper proposes a redundant, actuated, parasitic-motion-free 3-DOF 3RRS-RUS parallel stabilization platform. Based on the proposed 3RRS-RUS shipborne parallel stabilization platform, a Linear Active Disturbance Rejection Control (LADRC) approach, integrated with a Sliding Mode Disturbance Observer (SMDO), is developed. First, the mechanism is synthesized using screw theory, and its 2R1T 3-DOF characteristics are verified through parasitic motion analysis. Second, the inverse kinematics model is established. Third, the conventional LADRC is decoupled, and a new Linear Extended State Observer (LESO) together with its corresponding control law is designed. Moreover, an SMDO is incorporated into the motor’s three-loop control scheme to alleviate the estimation burden on the LESO and enhance the system’s disturbance rejection capability. Finally, experimental validations were carried out on both the CSPACE and SimMechanics platforms. The results demonstrate that the proposed SMDO–LADRC achieves superior tracking performance, high robustness, and strong disturbance rejection capability, The tracking errors along the RX, RY, and Z axes were reduced by 6.5%, 1.1%, and 16.6%, respectively, compared with the conventional LADRC, while also confirming the feasibility of the newly designed 3-DOF 3RRS-RUS shipborne parallel stabilization platform. Full article

Remote sensing video (RSV) provides continuous, high spatiotemporal earth observations that are increasingly important for environmental monitoring, disaster response, infrastructure inspection and urban management. Despite this potential, operational use of video streams is hindered by very large data volumes, heterogeneous acquisition platforms, inconsistent preprocessing practices, and the absence of standardized formats that deliver data ready for immediate analysis. These shortcomings force repeated low-level computation, complicate semantic extraction, and limit reproducibility and cross-sensor integration. This manuscript presents a principled multi-level analysis-ready data (ARD) model for remote sensing video, named VideoARD, along with VideoCube, a spatiotemporal management and query infrastructure that implements and operationalizes the model. VideoARD formalizes semantic abstraction at scene, object, and event levels and defines minimum and optimal readiness configurations for each level. The proposed pipeline applies stabilization, georeferencing, key frame selection, object detection, trajectory tracking, event inference, and entity materialization. VideoCube places the resulting entities into a five-dimensional structure indexed by spatial, temporal, product, quality, and semantic dimension, and supports earth observation OLAP-style operations to enable efficient slicing, aggregation, and drill down. Benchmark experiments and three application studies, covering vessel speed monitoring, wildfire detection, and near-real-time three-dimensional reconstruction, quantify system performance and operational utility. Results show that the proposed approach achieves multi-gigabyte-per-second ingestion under parallel feeds, sub-second scene retrieval for typical queries, and second-scale trajectory reconstruction for short tracks. Case studies demonstrate faster alert generation, improved detection consistency, and substantial reductions in preprocessing and manual selection work compared with on-demand baselines. The principal trade-off is an upfront cost for materialization and storage that becomes economical when queries are repeated or entities are reused. The contribution of this work lies in extending the analysis-ready data concept from static imagery to continuous video streams and in delivering a practical, scalable architecture that links semantic abstraction to high-performance spatiotemporal management, thereby improving responsiveness, reproducibility, and cross-sensor analysis for Earth observation. Full article

With an area of 13,000 km² and more than 60 outlet glaciers (tidewater or lake-terminating), the Southern Patagonian Icefield (SPI) stores a substantial volume of freshwater, and its accelerating melt contributes to global sea level rise. In addition to monitoring frontal retreat and ice thinning, tracking near-terminus glacier surface velocity can provide key insight into glacier dynamics. Here, we aimed to understand the current state of the SPI and to explore the dynamic restructuring of the glaciers in comparison with previous results. Considering that ice velocity acceleration near termini can be indicative of a drastic ice thinning and calving, during 2019–2020, we investigated the surface velocity of glaciers in the SPI using feature and speckle tracking. We calculated velocity maps (450 in total) from Sentinel-1 SAR images. Velocity ranged from 0 to 6571 myr⁻¹ for the whole study period, taking into account the 846 one square kilometer subsamples. Mean values of the topographic parameters (elevation, slope, aspect) have variable correlation with the mean velocity values, while mean ice thickness does not have a strong correlation with velocity. Nevertheless, mean velocities show association between near-frontal motion acceleration and calving, as observed in tidewater glaciers and four lake-terminating glaciers. Considering along-length changes in the glaciers, it is found that there are glaciers with upward increasing velocities, downward increasing velocities, and with a single velocity peak and multiple velocity peaks. Comparing our measurements with previous studies, we found major dynamic changes in several glaciers. A massive calving event at Pío XI Glacier significantly affected its velocity for months. The slowdown observed at 13–14 km from the terminus of the Jorge Montt Glacier contrasts with all previous studies that showed an acceleration of the glacier in this area. Our observations indicate rapid changes in some of the SPI glaciers, which suggests their unstable state. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent worldwide and represents a growing healthcare challenge due to its risk of progression and association with metabolic comorbidities. Extracellular vesicles (EVs), nanosized membrane-bound particles mediating intercellular communication, have emerged as candidate biomarkers in multiple diseases. This study aimed to characterize serum EV profiles in MASLD patients, stratified into obese and lean groups using a body mass index cutoff of 23 for Asians. Methods: We enrolled 170 MASLD patients, 83 obese (median age 50, range 20–80) and 87 lean (median age 50, range 20–87), along with 57 non-MASLD controls (median age 44, range 21–86). Serum EV concentrations and particle sizes were quantified using nanoparticle tracking analysis and correlated with clinical and laboratory parameters. EV cargo proteins, including tetraspanins (CD9, CD63) and lipid droplet-associated perilipins (PLIN2, PLIN3), were assessed by Western blotting. Results: Obese MASLD patients displayed marked biochemical abnormalities, whereas lean MASLD patients showed levels comparable to non-MASLD controls. Nevertheless, serum EV concentrations were elevated in both the obese and lean MASLD groups. Importantly, in lean MASLD, EV levels correlated strongly with disruptions in lipid and glycemic homeostasis. Furthermore, a reduction in the PLIN3/CD63 ratio was observed in EVs isolated from lean MASLD patients. Conclusions: Circulating EVs are elevated in both obese and lean MASLD, but lean patients demonstrate a distinctive decrease in the EV PLIN3/CD63 ratio. These findings highlight the potential of EV profiling to uncover disease heterogeneity and to inform risk stratification in MASLD. Full article

Structures are susceptible to external impacts over the long term, resulting in various types of damage. An online, accurate assessment of the severity of damage is the basis for formulating subsequent maintenance and reinforcement plans. In this work, an online damage identification method based on the Adaptive Extended Kalman Filter (AEKF) is proposed. Initially, the vibration signals of a concrete-filled steel tubular (CFST) test structure subject to multiple lateral impacts are processed, and signals before and after damage inception are spliced to track damage evolution. Subsequently, the natural frequencies extracted from the signals before and after damage inception, along with the amplitude of the damage itself, are integrated into the state vector to build a nonlinear state transfer and observation model, allowing estimation of the dynamic flexural stiffness of the structure. To further improve the problem solution in the presence of signal losses due to sensor detachment or breakage, missing signals are reconstructed using the weighted matrix pencil (MP), thereby ensuring the continuity and stability of the AEKF filtering process. By comparing the results with the actual damage state, the proposed method is shown to effectively track the gradual reduction in flexural stiffness and to verify its feasibility for providing reliable support for online monitoring and damage assessment. Full article

Sediment erosion is a persistent problem that leads to the deterioration of hydro-turbines over time, ultimately causing blade failure. This paper analyzes the dynamics of sediment in water and its effects on a small Kaplan turbine. Flow data is obtained independently and transferred to a separate Lagrangian-based finite element code, which tracks particles throughout the computational domain to determine local impacts and erosion rates. This solver uses a random walk approach, along with statistical descriptions of particle sizes, numbers, and release positions. The turbine runner features significantly twisted blades with rounded corners, and complex three-dimensional (3-d) flow related to leakage and secondary flows. The results indicate that flow quality, particle size, concentration, and the relative position of the blades against the vanes significantly influence the distribution of impacts and erosion intensity, subsequently the local eroded mass is cumulated for each element face and averaged across one pitch of blades. At the highest concentration of 2500 mg/m³, the results show a substantial erosion rate from the rotor blades, quantified at 4.6784 × 10⁻³ mg/h and 9.4269 × 10⁻³ mg/h for the nominal and maximum power operating points, respectively. Extreme erosion is observed at the leading edge (LE) of the blades and along the front part of the pressure side (PS), as well as at the trailing edge (TE) near the hub corner. The distributor vanes also experience erosion, particularly at the LE on both sides, although the erosion rates in these areas are less pronounced. These findings provide essential insights into the specific regions where protective coatings should be applied, thereby extending the operational lifespan and enhancing overall resilience against sediment-induced wear. Full article

In the semi-desert aeolian sand areas of Northern China, surface deformation monitoring with SAR is challenged by loss of coherence due to mobile dunes, seasonal vegetation changes, and large-gradient, nonlinear subsidence from underground mining. This study utilizes PALSAR-2 (L-band, 3 m resolution) and Sentinel-1 (C-band, 30 m resolution) data, applying InSAR and Offset tracking methods combined with differential, Stacking, and SBAS techniques to analyze deformation monitoring effectiveness and propose an efficient dynamic monitoring strategy for the Shendong Coalfield. The main conclusions can be summarized as follows: (1) PALSAR-2 data, which has advantages in wavelength and resolution (L-band, multi-look spatial resolution of 3 m), exhibits better interference effects and deformation details compared to Sentinel-1 data (C-band, multi-look spatial resolution of 30 m). The highly sensitive differential-InSAR (D-InSAR) can promptly detect new deformations, while Stacking-InSAR can accurately delineate the range of rock strata movement. SBAS-InSAR can reflect the dynamic growth process of the deformation range as a whole, and SBAS-Offset is suitable for observing the absolute values and morphology of the surface moving basin. The combined application of Stacking-InSAR and Stacking-Offset methods can accurately acquire the three-dimensional deformation field of mining-induced strata movement. (2) The spatiotemporal process of surface deformation caused by coal mining-induced strata movement revealed by InSAR exhibits good correspondence with both the underground mining progress and the development of ground fissures identified in UAV images. (3) The maximum displacement along the line of sight (LOS) measured in the mining area is approximately 2 to 3 m, which is close to the 2.14 m observed on site and aligns with previous studies. The calculated advance influence angle of the No. 22308 working face in the study area is about 38.3°. The influence angle on the solid coal side is 49°, while that on the goaf side approaches 90°. These findings further deepen the understanding of rock movement and surface displacement parameters in this region. The dynamic monitoring strategy proposed in this study is cost-effective and operational, enhancing the observational effectiveness of InSAR technology for surface deformation due to coal mining in this area, and it enriches the understanding of surface strata movement patterns and parameters in this region. Full article

Deploying autonomous vehicles in urban mobility systems promises significant improvements in safety, efficiency, and sustainability. On the other hand, running these vehicles in the continuously changing and often uncertain conditions of modern cities turns out to be a major challenge. These cars need advanced systems that can continuously change in order to observe conditions. This paper puts forward a new way that brings together multiple LIDAR sensors for the real-time spotting and following of objects, along with adaptive motion planning methods made to handle the difficulties of city traffic. Using LIDAR-based mapping for environmental modeling and predictive tracking techniques helps the system build a richly detailed, consistently updating depiction of surroundings that supports accurate and quick decisions. Another feature of the system is dynamic path planning that ensures safe navigation by considering traffic, pedestrian movement, and road conditions. Simulations carried out in highly dense urban scenarios show improvement in collision avoidance, path-planning optimization, and response to environmental dynamics. Such outcomes prove that combining multi-LIDAR tracking and adaptive motion planning contributes significantly to the performance and safety of an autonomous vehicle when operating in very complex urban conditions. Full article

Background: In Mexico, the measles vaccine was first introduced in 1971. The last case of measles acquired through endemic transmission was recorded in 1995. In 1998, the monovalent measles vaccine was replaced by the combined measles–mumps–rubella (MMR) vaccine. The MMR vaccination schedule consists of two doses: the first is administered at 12 months of age, and the second is administered at either 18 months or 6 years of age. Materials and Methods: A retrospective analysis was conducted using secondary data from 2006 to 2024. Vaccine procurement and administration records from IMSS, ISSSTE, and SSA were reviewed to evaluate the performance of both the MMR and MR programs, focusing particularly on the trends in coverage and data consistency across institutions. Results: The analysis revealed persistent inconsistencies between vaccine procurement and administration for both the MMR and MR vaccines across all institutions. Several years exhibited notable mismatches, including surpluses and deficits in the administered doses relative to their procurement. Between 2006 and 2024, only 69 million of the 91.6 million required MMR doses were administered in Mexico, leaving a deficit of approximately 22.5 million doses (25% of the target population). For MR, a cumulative deficit of approximately 24.6 million procured but unadministered doses was identified. National coverage remained suboptimal, with significant variability across years and institutions. Comparisons with WHO and ENSANUT data indicated marked discrepancies. The seroprevalence findings, along with the 2025 measles outbreak, confirm significant gaps in immunity. Discussion: This study highlights systemic challenges in Mexico’s MMR vaccination program, including inconsistencies in vaccine procurement, administration, and reported coverage across institutions. Overestimated official MMR coverage rates and unclear target definitions for MR contribute to program inefficiencies and missed vaccination opportunities. The resurgence of measles in 2025, along with persistently high incidences of mumps, aligns with the observed immunity gaps, although a direct causal relationship cannot be established from this study. These findings are consistent with previous national studies and seroprevalence data. Conclusions: Despite limitations in the data, this study effectively evaluated the performance of Mexico’s MMR vaccination program, identifying critical gaps in coverage, data reliability, and operational alignment. The findings underscore the need for improved procurement planning, harmonized coverage estimates, and robust monitoring systems. To address the existing gaps in immunity, catch-up campaigns should prioritize the use of the MMR vaccine over MR. Strengthening nominal coverage tracking and implementing evidence-based strategies are essential to restoring public trust and maintaining the goals of measles elimination. Full article

We model the short-period cataclysmic variable EZ Lyn with MESA binary evolution and infer its present-day parameters through a staged statistical search. First, we compute a coarse grid of tracks in $(M_{1,0},P_0)$ at fixed $M_{2,0}$ and rank snapshots by a profile likelihood. We then resample the neighbourhood of the minimum to build a refined $\Delta {\chi }^2$ surface. Finally, we sample this surface with an affine-invariant MCMC to obtain posteriors, using a likelihood that treats the one-sided constraint on the donor temperature and the ambiguity of component roles in the binary output. The best-fit snapshot reproduces the observables and identifies EZ Lyn as a period bouncer with a substellar donor. We infer $M_{\mathrm{WD}}=0.850\pm 0.019{\mathrm{M}}_{\odot }$, $M_2=0.0483\pm 0.0137{\mathrm{M}}_{\odot }$, $R_{\mathrm{WD}}=0.0092\pm 0.0001{\mathrm{R}}_{\odot }$, $R_2=0.099\pm 0.005{\mathrm{R}}_{\odot }$, $T_{\mathrm{WD}}=11,500\pm 20\mathrm{K}$, and $T_2=1600\pm 50\mathrm{K}$. The instantaneous mass-transfer rate at the best-fit snapshot is $\dot{M}=3.66\times {10}^{-11}{\mathrm{M}}_{\odot }{\mathrm{yr}}^{-1}$, consistent with the secular range implied by the white-dwarf temperature. Independent checks from the Roche mean-density relation, surface gravities, and the semi-empirical donor sequence support the solution. In population context, EZ Lyn lies in the period-minimum spike and on the low-mass tail of the donor mass–period plane. The classification is robust to modest displacements along the shallow $\Delta {\chi }^2$ valley. We release inlists, tracks, and analysis scripts for reproducibility. Full article

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios. Full article

In this paper, a model-based approach for the identification of the railway vertical track alignment from simulation data is presented. The proposed methodology is based on the application of the unknown input observer algorithm. The model of a conventional train is used to simulate the acceleration levels that vehicle-mounted sensors (e.g., on the bogies and carbody) would measure during operation. Simulations are carried out at a constant speed on both straight and curved tracks, including different types of track geometry components (namely longitudinal level, alignment, and cross-level) to assess the algorithm capability to identify the input irregularity. The primary focus is on the identification of mean vertical track alignment, a critical irregularity component for safety issues. In the analysed cases, the comparison between the measured and reconstructed signal histories are quite satisfactory, with maximum errors in the order of 15% and 29% along straight and curved tracks. Comparing the frequency content of the signals, a significantly higher degree of accuracy is observed (with maximum errors of 5–10% depending on the track layout), which demonstrates that the proposed methodology is suitable for track irregularity identification and monitoring purposes using an instrumented vehicle. Full article