Search Results (37)

Surface faulting poses an earthquake-related hazard with direct consequences for infrastructure and high-risk facilities. Probabilistic Fault Displacement Hazard Analysis (PFDHA) is widely used to estimate the annual frequency of exceedance (AFOE) of specific displacement values at sites on or near active faults. This approach requires numerous input parameters related to fault characterization and coseismic displacement distribution, yet few studies have examined how these parameter choices affect hazard results. Thus, we conduct an analysis following a One-At-a-Time (OAT) strategy, in which a single parameter is varied with respect to three kinematic-specific baselines. We explored the PFDHA outputs obtained allied to the broadly adopted regression models and scaling laws available in the literature up to 2023. We compared the hazard curves obtained for principal faulting from each calculation to a baseline parametrization, and we computed the percentage difference in AFOE, given a displacement amount, with respect to such a baseline. We obtained values in the interval −100% to +200%, computed within the displacement interval adopted for the hazard calculation, attesting that empirical regressions contribute significantly to hazard curve variations. Our sensitivity study could inform operative choices by practitioners and provides insights for optimizing data acquisition efforts in fault displacement hazard assessments. Full article

The biomechanics of frog jumping has been a subject of significant interest in both biology and engineering, driven by the high efficiency of their movement. This study presents the dynamic simulation of a frog’s complete jump cycle, from take-off to landing and re-stabilization, to advance the development of bio-inspired jumping robots for irregular terrains. As a primary contribution, and unlike previous studies that focus exclusively on the propulsion phase, this work addresses all stages, using direct servomotor actuation without mechanical energy storage. Biological joint kinematics were mathematically characterized using Cubic Smoothing Splines. By empirically tuning the smoothing parameter ($p$), the trajectories achieved the continuous differentiability required for electromechanical actuation. These curves were implemented into a 3D multibody simulation (Altair Inspire), where a PID-based tracking framework managed the mechanically nonlinear multibody dynamics governing the jump (arising from contact forces, impacts, and time-varying inertial effects) to ensure stabilization during the complex landing phase. Validating the model against previous studies, the simulation successfully achieved a maximum horizontal jump distance of 24.12 cm (4.02 body lengths) and a peak velocity of 1.45 m/s. The kinematic fidelity of the model was mathematically validated, yielding a maximum Normalized Root Mean Square Error (NRMSE) of 4.121% relative to biological reference trajectories. Furthermore, the robustness of the landing and re-stabilization phases was demonstrated through a continuous double jump covering a total distance of 45.83 cm. Finally, a dynamic scaling analysis was performed to evaluate the feasibility of implementing real motors. Ultimately, this study establishes a mathematically robust framework for replicating frog-inspired jumping dynamics, contributing a transferable methodology for the design and control of articulated bio-inspired robotic systems. Full article

Weight reduction and dynamic performance optimization are critical for airborne direct-drive VICTS satellite communication antennas, which require lightweight, high-speed, and high-precision rotation. Traditional vibration suppression methods, such as uniform support layout and added damping, rely heavily on empirical trial and error, lack targeted modal control, and cannot balance lightweight design with dynamic stiffness. To address these issues, this paper proposes a wave-theory-based dynamic modeling and rapid optimization method for multi-layer rotating components in direct-drive VICTS antennas. The kinematic model of the rotating ring and ball revolution excitation are derived using the annular wave equation and bearing kinematics. A Modal Blocking Mechanism is established: placing support balls at positions satisfying the half-wavelength constraint suppresses target mode shapes via wave interference, achieving vibration attenuation at the source. A homogenization equivalent method based on RVE is developed for irregular cross-section rings, yielding analytical expressions for in-plane equivalent elastic modulus and out-of-plane equivalent shear modulus. These parameters are integrated into the wave equation to analytically solve vibration modes, avoiding iterative finite element computations. A rapid multi-objective optimization framework is then constructed, minimizing the structural weight and maximizing the modal separation interval under dynamic stiffness and excitation frequency constraints. Numerical simulations, FE analysis, and prototype tests validate the method: the maximum analytical error is only 3.1%. Compared with uniform support designs, the optimized structure achieves a 40% weight reduction, a 40% increase in minimum modal separation, and a 65% reduction in the RMS tracking error. This work provides an efficient, deterministic dynamic design method for large-diameter ring structures, transforming vibration control from empirical adjustment into a precise, physics-informed optimization. Full article

This paper presents a theoretical framework for predicting molten jet breakup at the outlet of a rotating granulation system operating without forced excitation. The study focuses on the critical regime in which mechanical excitation is absent, and jet disintegration is governed solely by intrinsic hydrodynamic instabilities. The analysis is based on the linear stability theory of viscous liquid jets, employing the Rayleigh–Plateau and Tomotika approaches adapted to melt conditions typical of industrial granulation processes. The Navier–Stokes equations are formulated in a cylindrical coordinate system for an axisymmetric, incompressible viscous jet with appropriate kinematic and dynamic boundary conditions at the free surface. The breakup mechanism is characterized using key dimensionless parameters, including the Ohnesorge, Weber, Reynolds, and Capillary numbers, enabling identification of the dominant instability regime. Analytical expressions are derived for the most unstable wavelength, perturbation growth rate, breakup time, and characteristic droplet diameter. These relationships are evaluated for representative thermophysical properties of molten urea. Theoretical predictions obtained from classical Rayleigh theory, viscosity-corrected models, and modern empirical correlations show strong agreement, with deviations not exceeding 7%. Sensitivity analysis indicates limited dependence of the predicted droplet diameter on moderate variations in viscosity, surface tension, and jet velocity. The proposed model provides a physically grounded basis for predicting and controlling granule size distribution in rotating granulation systems operating without external mechanical excitation. Full article

Road transportation is a major contributor to global CO₂ emissions, yet the influence of road geometry on vehicular emissions remains insufficiently quantified under real-world conditions. This study investigates the effects of horizontal and vertical alignments on CO₂ emissions of a light-duty gasoline passenger vehicle using Portable Emissions Measurement System (PEMS) data collected along a 62.4 km highway section. Six geometric parameters longitudinal grade, cross slope, horizontal curve radius, horizontal curve length, vertical curve radius, and vertical curve length were analyzed in combination with second-by-second vehicle dynamics. The results indicate that transient CO₂ emissions exhibit substantial variability, with instantaneous emission rates exceeding 7.0 g/s under high-load conditions. Longitudinal slope gradient shows the strongest linear association with emission rate (r = 0.63), while speed and acceleration exhibit weaker but statistically significant correlations (r = 0.21 and r = 0.28, respectively). Vehicle Specific Power (VSP), representing integrated tractive power demand, demonstrates stronger association with instantaneous CO₂ emissions than individual kinematic variables. In contrast, cross slope and horizontal curvature parameters display minimal direct correlations under the tested highway conditions. A nonlinear polynomial regression model modestly improves explanatory performance relative to a linear formulation (R² = 0.21 versus 0.15; RMSE approximately 56 g/km), although a substantial portion of variability remains unexplained, reflecting the complexity of transient real-world processes. Overall, vertical alignment and transient driving conditions dominate CO₂ emission variability, while horizontal parameters play supplementary roles. These findings provide empirical evidence for refining emission models and highlight the importance of incorporating vertical alignment into sustainable roadway design and carbon reduction strategies. Full article

While anisotropic extrudate swell in polymer processing is fundamentally driven by physical viscoelastic recovery, this paper proposes a theoretical framework to explicitly isolate and map the purely geometric and kinematic components of this phenomenon. Serving as a mathematical proof-of-concept, a multi-velocity-centre hypothesis is proposed. By introducing a semi-empirical, lumped material-flow calibration parameter, the macroscopic diameter swell ratio is mathematically extended to the discrete local flow field of a rectangular slit die. To evaluate its validity, the analytical framework is subjected to a numerical test for kinematic consistency utilizing isothermal, inelastic power-law fluid CFD simulations, thereby separating geometric mapping from complex viscoelastic stress relaxation. Results indicate that analytical predictions show good agreement with CFD data (error < 5%) strictly within the core zone of high-aspect-ratio dies. However, due to the infinite-slit assumption, 3D flow kinematics near die edges induce velocity decay, leading to local deviations that require future empirical corrections. Although comprehensive physical extrusion experiments and non-isothermal viscoelastic coupling are required for industrial deployment, this semi-empirical kinematic mapping provides a foundational mathematical basis that could potentially inform future inverse die-profile design and shape distortion compensation. Full article

The dynamic approach integrates Global Positioning System and K-band range-rate (KRR) observations to enable precise orbit determination (POD) and gravity field recovery. However, background model uncertainties and temporal aliasing introduce frequency-dependent noise into the post-fit KRR residuals, thereby degrading overall solution accuracy. To mitigate these effects, empirical signals are typically modeled using either dynamic (DYN) or kinematic (KIN) parameterization strategies. Nevertheless, the combined use of DYN and KIN parameterizations remains largely unassessed, and their potential synergistic impact on POD and gravity field recovery merits systematic evaluation. This study evaluates the individual and joint impacts of DYN and KIN (DYN+KIN) on The Gravity Recovery and Climate Experiment (GRACE) Follow-On orbit accuracy and monthly gravity field recovery using nearly one year of 2019 data (excluding February due to severe data gaps). The refined solutions act as empirical temporal filters, effectively suppressing low-frequency components in KRR residuals, particularly below 1-cycle-per-revolution. Relative to nominal ambiguity-fixed reduced-dynamic orbits, the refined solutions mainly enhance the cross-track component, with DYN+KIN showing the largest improvement, while along-track precision experiences only minor (sub-millimeter) degradation. Overall three-dimensional orbit accuracy improves from 3.8 cm to 3.0 cm (DYN), 2.8 cm (KIN), and 2.8 cm (DYN+KIN). In terms of gravity field recovery, the DYN+KIN solution begins to exhibit more pronounced deviations from the other solutions beyond degree and order 30. Over oceanic regions, residual mass anomaly analysis shows that the DYN+KIN solution is associated with an approximately 16% higher noise level compared to the individual DYN and KIN strategies, which exhibit modest noise reductions relative to the nominal solution. The DYN+KIN also exhibits a dampened ~160-day periodicity in the temporal evolution of low-degree coefficients (e.g., C_2,0), likely due to spectral overlap between empirical parameter frequencies and low-degree gravity signal components. These results indicate that over-parameterization introduces spectral redundancy and absorbs geophysical signals, underscoring the need to balance parameter flexibility and signal fidelity in gravity recovery strategies. Full article

This paper presents a physics-consistent hybrid surrogate framework for simulating the mechanical behavior of reinforced concrete beams that utilize waste fired clay (WFC) as a partial substitute for cement. The main contribution is the integration of empirically observed deformation behavior with physics-informed learning to produce an interpretable, mechanically valid surrogate model. Full-field surface deformation fields were measured using Digital Image Correlation (DIC) under monotonic loading and processed through a convolutional neural network (CNN) to extract deformation- and crack-sensitive features. These features were integrated with experimentally measured stress–strain data within a Physics-Informed Neural Network (PINN) in which equilibrium and conditional constitutive monotonicity constraints were enforced through the loss function. A Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was utilized as a downstream parametric exploration tool to examine trade-offs among maximum load capacity, material cost, and embodied CO₂ inside a constrained mixture-design space. Model interpretability was assessed by SHapley Additive exPlanations (SHAP), indicating that deformation-driven kinematic factors predominantly influence stress prediction, whereas WFC content and reinforcement parameters have a secondary, mixture-level impact. The resulting framework achieves enhanced predictive accuracy (R² = 0.969) relative to its individual components and operates as an offline, physics-calibrated surrogate rather than a real-time digital twin, providing a reliable and interpretable basis for structural assessment and sustainability-oriented design evaluation of WFC-modified reinforced concrete beams. Full article

Background/Objectives: Exercise-induced fatigue is a fundamental component of running performance and training, yet it is also implicated in altered movement mechanics and increased injury risk. While numerous studies have examined fatigue-related biomechanical changes during running, findings remain fragmented across biomechanical domains and fatigue modalities. The purpose of this systematic review was to synthesize contemporary evidence on the effects of fatigue on lower-limb biomechanics during running and to interpret the potential injury relevance of these adaptations. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science for original empirical studies published between January 2010 and December 2025. Eligible studies involved human participants performing running or running-related tasks, applied an explicit fatigue protocol, and reported quantitative lower-limb biomechanical outcomes. Study selection followed PRISMA 2020 guidelines. Data extraction included participant characteristics, fatigue protocols, biomechanical measures, instrumentation, and key findings. Methodological quality was assessed using the Cochrane Risk of Bias 2 (RoB-2) tool. Due to substantial methodological heterogeneity, findings were synthesized narratively. Results: Twenty-four studies met the inclusion criteria. Across studies, fatigue consistently altered spatiotemporal parameters, joint kinematic and kinetic variables, spring-mass behavior, impact loading, coordination variability, neuromuscular output, and inter-limb symmetry. Common adaptations included increased ground contact time, reduced ankle joint power and stiffness, increased joint range of motion, elevated impact loading, and greater movement variability. These changes reflected reduced mechanical efficiency and a redistribution of mechanical load from distal to proximal joints, particularly toward the knee and hip. Similar fatigue-related biomechanical patterns were observed in both laboratory-based and real-world endurance running conditions. Conclusions: Exercise-induced fatigue produces systematic and injury-relevant alterations in lower-limb biomechanics during running. These adaptations may preserve short-term performance but create mechanical conditions associated with increased susceptibility to overuse and non-contact injuries. Integrating fatigue-aware biomechanical assessment, neuromuscular conditioning, and individualized load management strategies may help mitigate adverse fatigue-related adaptations. Full article

Background: Video-based biomechanical running gait analysis is widely used to optimise technique, guide footwear selection, and identify orthopaedic risk factors. Despite the increasing availability of such assessments, it is often assumed—without strong empirical support—that key kinematic parameters of running gait remain stable over short periods of time. This study aimed to examine the short-term stability of key joint angles during running using a standard 2D video-based kinematic analysis. Specifically, it was investigated whether these angles change within the first 4 min of treadmill running under three defined conditions: barefoot at 12 km h⁻¹, shoed at 12 km h⁻¹, and shoed at 14 km h⁻¹, in a homogeneous sample of twelve young, trained, male recreational soccer players. Methods: Participants completed three four-minute runs. Joint angles were quantified manually from 2D video recordings. Temporal variation was analysed using repeated-measures statistics, intraclass correlation coefficients (ICCs), and minimal detectable changes (MDCs). Results: Six out of nine joint angles showed statistically significant temporal changes, mainly in hip extension, knee flexion, the Duchenne angle, the Trendelenburg angle, the leg axis angle, and heel-bottom angle. Lower leg angle and Achilles tendon angle remained stable. ICCs showed moderate to excellent agreement, indicating high within-session consistency across all angles. Discussion: Under the applied study protocol, significant short-term variations were observable in several joint angles during the first four minutes of running. These findings highlight the importance of analysing multiple strides and considering measurement uncertainty when interpreting short-duration running kinematics. Full article

Accurate multi-leaf collimator (MLC) motion prediction is a prerequisite for precise dose delivery in advanced techniques such as IMRT and VMAT. Traditional patient-specific quality assurance (QA) methods remain resource-intensive and prone to physical measurement uncertainties. This study aimed to develop machine learning (ML) models to predict delivered MLC positions using kinematic parameters extracted from DICOM-RT plans for the Elekta Versa HD system. A dataset comprising 200 patient plans was constructed by pairing planned MLC positions, velocities, and accelerations with corresponding delivered values parsed from unstructured trajectory logs. Four regression models, including linear regression (LR), were trained to evaluate the deterministic nature of the Elekta servo-mechanism. LR demonstrated superior prediction accuracy, achieving the lowest mean absolute error (MAE) of 0.145 mm, empirically confirming the fundamentally linear relationship between planned and delivered trajectories. Subsequent dosimetric validation using ArcCHECK measurements on 17 clinical plans revealed that LR-corrected plans achieved statistically significant improvements in gamma passing rates, with a mean increase of 2.24% under the stringent 1%/1 mm criterion (p < 0.001). These results indicate that the LR model successfully captures systematic mechanical signatures, such as inertial effects. This study demonstrates that a computationally efficient LR model can accurately predict Elekta MLC performance, providing a robust foundation for implementing ML-based virtual QA. This approach is particularly valuable for time-sensitive workflows like adaptive radiotherapy (ART), as it significantly reduces reliance on physical QA resources. Full article

Global navigation satellite system (GNSS) real-time kinematic (RTK) has been widely applied in landslide monitoring and warning, since it can provide real-time and high-precision three-dimensional deformation information in all weather and all the time. The Kalman filter is often adopted for parameter estimation in GNSS RTK positioning since it can effectively suppress the observational noise and improve the positioning accuracy and reliability. However, the discrepancy between the empirical state model in the Kalman filter and the actual state of the monitoring object could lead to large positioning errors or even the divergence of the Kalman filter. In this contribution, we propose a novel rapid deformation identification and adaptive filtering approach with GNSS time-differenced carrier phase (TDCP) under different scenarios for landslide monitoring. We first present the methodology of the proposed TDCP-based rapid deformation identification and adaptive filtering approach for GNSS RTK positioning. The effectiveness of the proposed approach is then validated with a simulated displacement experiment with a customized three-dimensional displacement platform. The experimental results demonstrate that the proposed approach can accurately and promptly identify the rapid between-epoch deformation of more than approximately 1.5 cm and 3.0 cm for the horizontal and vertical components for the monitoring object under a complex observational environment. Meanwhile, it can effectively suppress the observational noise and thus maintain mm-to-cm-level monitoring accuracy. The proposed approach can provide high-precision and reliable three-dimensional deformation information for GNSS landslide monitoring and early warning. Full article

The aim of the article is to present the impact of blending diesel fuel with tire pyrolysis (TPO) oil on the changes in the fuel’s rheological properties and to evaluate these changes in the context of meeting legal requirements for various types of fuels. This research presents the impact of normative D100 diesel oil with TPO as an admixture on the rheological properties of the blends. Measurements are made for the content of TPO in the blend equal to 5, 7, 10, 15, and 20% m/m. In addition, the reference measurements are made for pure diesel oil and pure pyrolytic oil. Kinematic viscosity density, dynamic viscosity, viscosity index, pour point, cloud point, and cold filter plugging point are determined. The density of each sample is found at 15, 20, 30, 40, 50, 60, 70, 80, 90, and 100 °C. Viscosity is determined at the reference temperatures of 20, 40, and 100 °C, which are typically used as reference temperatures for petroleum products. Approximating models are built for all the analyzed parameters, which can be used in future studies. The fit of each model to empirical data is evaluated using the coefficient of determination R². At the same time, the individual values of the analyzed indicators are compared to the limit values specified in selected standards and regulations, thus allowing us to assess the usefulness of individual fuels in terms of compliance with effective and reliable engine operation requirements. The fuels under study fulfill the normative requirements for the parameters for marine distillate fuels for blends with a pyrolysis oil content of 0–20% m/m and the requirements for standard-grade diesel oils indicated in the Regulation of the Minister of Economy of Poland for blends with a pyrolysis oil content of 0–7% m/m. Full article

Rockfalls pose significant threats to infrastructure, transportation routes, and human safety in mountainous regions, making them a critical concern in natural hazard and risk management. Accurate prediction of rockfall behavior is essential for designing effective mitigation strategies. The normal coefficient of restitution (R_n) is a key kinematic parameter for modeling falling rock dynamics, specifically quantifying the energy retained after collision between a rock and a slope surface. While this parameter is not directly used in prevention design, it is crucial for predicting the movement and trajectory of falling rocks and can indirectly support the development of more effective hazard mitigation strategies. However, R_n is influenced by multiple factors, including slope angle, surface material, falling rock shape, and initial velocity. The interactions among these factors make a precise prediction of R_n particularly challenging. Existing theoretical and empirical formulas typically consider individual factors in isolation, often neglecting their interactions, which leads to significant discrepancies in the results. To address this gap, we conducted a series of laboratory physical model tests to investigate the interactions among highly sensitive controlling factors and improve the accuracy of R_n prediction. A self-designed release apparatus, coupled with a high-speed recording and analysis system, was used to capture full kinematic data during rockfall collisions on slopes. This study not only examined how the main controlling factors and their interactions affect R_n but also developed a multi-factor interaction regression model, which was verified using on-site test data. The results show that the effect of the main controlling factors decreases in the following order: falling rock shape, slope surface material, initial velocity, and slope angle. Considering that falling rock shape and slope surface material cannot be quantitatively evaluated, the shape factor (η) and material factor (A_slope) are proposed to represent two controlling factors, respectively. Specifically, increases in η, A_slope, initial velocity, and slope angle are negatively correlated with R_n. Highly significant interactions were observed among falling rock shape–slope surface material, falling rock shape–initial velocity, falling rock shape–slope angle, slope surface material–initial velocity, and falling rock shape–slope surface material–initial velocity. These interactions mitigate the R_n reduction, resulting in a weaker effect than the stacking effect of the individual factors. The phenomenon is primarily attributed to the fact that high-level η, A_slope, initial velocity, and slope angle diminish the effect of intersecting factors. Finally, a comparison of the multi-factor interaction model with on-site tests and empirical formulas revealed the accuracy of the proposed model. Full article

The prediction of debris flows is essential for safeguarding infrastructure and minimizing the economic losses associated with the hazards. Traditional empirical and theoretical models, while providing foundational insights, often struggle to capture the complex and nonlinear behaviors inherent in debris flows. This study aims to enhance debris flow prediction by integrating theoretical modeling with data-driven approaches. We model debris flow as a viscoplastic fluid, employing the Herschel–Bulkley rheological model to describe its behavior. By combining the kinematic wave model with lubrication theory, we develop a comprehensive theoretical framework that encapsulates the mechanical physics of debris flows and identifies key governing parameters. Numerical solutions of this theoretical model are utilized to generate an extensive training dataset, which is subsequently used to train a support vector regression (SVR) model. The SVR model targets slide depth and velocity upon impact, using explanatory variables including yield stress, material density, source area depth and length, and slope length. The model demonstrates high predictive accuracy, achieving coefficients of determination $R^2$ of 0.956 for slide depth and 0.911 for slide velocity at impact. Additionally, the relative residuals $\sigma$ are primarily distributed within the range of −0.05 to 0.05 for both slide depth and slide velocity upon impact. These results indicate that the proposed hybrid model not only incorporates the fundamental physical mechanisms governing debris flows but also significantly enhances predictive performance through data-driven optimization. This study underscores the critical advantage of merging physical models with machine learning techniques, offering a robust tool for improved debris flow prediction and risk assessment, which can inform the development of more effective early warning systems and mitigation measures. Full article