Search Results (89)

Capillary imbibition is the process by which liquids are absorbed into porous materials as a result of capillary pressure differences at the pore scale. Accurate characterization of imbibition dynamics, particularly in the presence of gravitational potential, is essential for understanding fluid transport in diverse systems such as soil, fractured rocks, filtration media, and plant roots. This study presents systematic imbibition experiments using filter papers with pore sizes of 2.5 µm, 11 µm, and 20 µm, each inclined at 80° to quantify the influence of gravitational potential on imbibition behavior. For horizontally positioned samples, the imbibition front propagated radially and symmetrically, exhibiting a power law dependence on time. The measured temporal exponents ranged from 0.386 to 0.403, consistently lower than the theoretical value of 1/2 predicted by the Lucas–Washburn law. With increasing permeability, the temporal exponent approached the Washburn limit, indicating a marked dependence of imbibition dynamics on pore structure. For the inclined configuration at an 80° angle, the imbibition fronts remained nearly circular but exhibited a pronounced displacement of the front center toward gravity. This displacement increased with permeability, from approximately 0.497 cm for the 11 µm filter paper to 3545 cm for the 20 µm filter paper, highlighting the combined effects of permeability and gravitational potential on fluid movement. Furthermore, the advance of the imbibition front was significantly slower in the smallest pores (2.5 µm) compared to the larger ones. Experimental results were evaluated against a theoretical model proposed by Medina, demonstrating moderate quantitative agreement at early times, when gravitational potential effects are less significant. These findings confirm that both the temporal scaling exponent and the spatial evolution of the imbibition front are governed by the porous medium’s permeability and inclination angle, providing experimental evidence of deviations from ideal Washburn behavior in real porous systems. Full article

The quality of electrical power on distribution networks depends heavily on the performance of the harmonic filtering method implemented according to the operating conditions of the system under study. This article proposes a new experimental approach that allows us to determine the dynamic behavior of the harmonic signature of the nonlinear load connected to the electrical network. The ultimate goal is to propose a new law for extracting the reference currents required during the overall online harmonic filtering process using a hybrid filter. This offers advantages in robustness and accuracy during variations in the electrical network compared to the classical methods used in the current literature. The methodological approach consists of selecting several nonlinear loads according to specific profiles and technical characteristics, then experimentally analyzing their harmonic signatures over time to obtain new models for extracting reference currents that will ultimately be faster to implement. In a decentralized global harmonic filtering strategy using a TLC adaptive hybrid filter compliant with the IEEE-519-2022 standard, the results obtained offer THD (total harmonic distortion) values of 1.88%, 3.29%, and 2.78% in three-phase currents, with a reduced DC voltage consumption of 105 V for the inverter. This contrasts with similar models in the current literature, which require input DC voltages exceeding 850 V for identical performance. This work therefore represents a major contribution to new models for extracting experimentally obtained reference currents, enabling the optimization of power quality on electrical networks through the use of a hybrid filter. Full article

Global sediment reduction threatens deltaic sustainability and channel stability. While climatic and anthropogenic drivers are recognized, their cross-scale interactions remain poorly understood. This study investigated area-specific sediment yield (SSY) and its driving mechanisms across 14 stations (1.9 × 10⁴ to 1.0 × 10⁶ km²) in the Upper Yangtze River Basin (UYRB) from 1960 to 2018 using PLS-SEM and power-law scaling. Results show that by 2018, reservoir capacity reached 165.5 billion m³, regulating 38% of annual runoff. SSY significantly declined at 12 of 14 stations, with abrupt change points clustering around 1985. We found that intensive human interventions have fundamentally restructured the natural scale dependency of SSY, with the scaling exponent (β) shifting from a stable near-zero value to violent fluctuations (−0.2 to 0.5). Temporally, the dominant driver transitioned from hydro-climatic factors to dam-induced regulation. Spatially, the “filtering effect” of dams intensified with increasing drainage area, whereas smaller watersheds remained disproportionately sensitive to extreme precipitation. This scale-based divergence reveals a critical vulnerability: while mega-dams mitigate sediment at the basin scale, smaller catchments face elevated risks of high sediment delivery under intensifying climate extremes. These findings provide evidence of human-induced scaling instability in a large river system and highlight the necessity of scale-sensitive governance to ensure geomorphic and ecological resilience worldwide. Full article

In active debris removal missions in low Earth orbit, the semi-major axis difference between a service platform and its target can be large. Analytical relative dynamics models used in formation-flying applications typically retain only the first-order expansion in the orbital element differences; at large separations, the discarded higher-order terms—particularly the power-law dependence on the semi-major axis—introduce systematic along-track drift that degrades the propagation accuracy. This paper derives the power-law truncation correction, a closed-form additive vector that exactly compensates the truncated semi-major-axis power-law remainder, together with a consistent Jacobian correction for the extended Kalman filter covariance prediction. The state dimension and state transition matrix structure remain unchanged. Propagation tests over semi-major axis differences of 36–146 km yield ten-revolution terminal position errors of 0.008–0.065 km for the corrected models, compared with tens to hundreds of kilometers for the uncorrected first-order models and 0.1–8 km for the second-order state transition tensor. In 500-run Monte Carlo angles-only filtering experiments, the corrected filter reduces the median terminal position error by one to nearly three orders of magnitude relative to the uncorrected model. A process noise sensitivity study confirms robustness to calibration uncertainty across two orders of magnitude at a lower computational cost and with simpler implementation than higher-order tensor methods. Full article

Distributed beamforming in UAV swarms requires fast and accurate carrier-phase alignment under sparse connectivity and propagation-induced phase bias. This paper proposes a physics-aware decentralized synchronization framework for quasi-static UAV swarm beamforming by integrating momentum-accelerated Metropolis–Hastings consensus with position-aided phase pre-compensation. To preserve phase evolution on the circular manifold, a sinusoidal coupling law is adopted, while the momentum term improves convergence in sparse random geometric graphs. A propagation model is further established to characterize how geometric separation and ranging uncertainty translate into residual phase error and coherent power loss. Under small-signal conditions, local stability is analyzed, and Monte Carlo simulations are conducted to evaluate convergence, synchronization accuracy, robustness, and beam-focusing performance. Results show that, at 2.4 GHz with low-centimeter ranging uncertainty, the proposed method achieves sub-wavelength synchronization accuracy while providing an effective balance among convergence speed, accuracy, and complexity. Compared with standard Metropolis–Hastings, fixed-weight, and other accelerated consensus methods, the proposed scheme converges faster over most sparse topologies. Although its steady-state accuracy is slightly lower than that of filter-based predictive methods such as KF-DFPC in some cases, those schemes incur higher implementation and computational overhead. Therefore, from the perspectives of decentralized realization and practical deployment, the proposed method is more suitable for lightweight phase synchronization in distributed UAV swarms. Full article

Fungal communities exhibit strong spatial and environmental structuring across forest ecosystems, yet the drivers shaping their diversity patterns remain incompletely understood. In this study, we combined multivariate ordination, clustering analyses, and Zeta diversity (ζ-diversity) metrics to characterize fungal assemblages across environmental gradients. Canonical Correspondence Analysis (CCA) revealed that fungal community composition was significantly associated with climatic variables, particularly seasonal precipitation, thermal variation, and elevation. Hierarchical and K-means clustering identified coherent community clusters that differed in species richness and alpha diversity. Bray–Curtis distances and a Ward-based dendrogram further supported this separation, revealing a clear hierarchical structure in community similarity. Zeta diversity analysis indicated a slower species turnover, suggesting niche assimilation and habitat homogenization. Furthermore, the grouping of fungal assemblages followed a power-law model, emphasizing the role of deterministic environmental filtering. Critically, our findings reveal that only 1208 (33.5%) of the 3606 recorded species are present within existing Protected Natural Areas (PNAs), indicating a significant conservation gap. Together, these results provide an integrated ecological understanding of fungal diversity patterns, highlighting how climate–topography interactions structure communities and emphasizing the urgent need to align conservation strategies with these environmental drivers. Full article

In this work, we consider a simple bistable motif constituted by a self-enhancing Transcription Factor (TF) and its mRNA with non-instantaneous dynamics. In particular, we mainly numerically investigated the impact of bounded stochastic perturbations of Sine–Wiener type affecting the degradation rate/binding rate constant of the TF on the phase-like transitions of the system. We show that the intrinsic exponential delay in the TF positive feedback, due to the presence of a mRNA with slow dynamics, deeply affects the above-mentioned transitions for long but finite times. We also show that, in the case of more complex delays in the feedback and/or in the translation process, the impact of the extrinsic stochasticity is further amplified. We also briefly investigate the power-law behavior (PLB) of the averaged energy spectrum of the TF by showing that, in some cases, the PLB is simply due to the filtering nature of the motif. A similar analysis can also be applied to biological models having a qualitatively similar structure, such as the well-known Capasso and Paveri–Fontana model of cholera spreading. Full article

Replicability of Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) derived from drone photogrammetry is important to understand the extent to which time-series are exposed to methodological noise and conceal real environmental changes. Root mean square error (RMSE) distribution metrics (median/IQR) were used as indicators of replicability across seven drone survey setups, three dense matching scales, and 13 ground point filters in a challenging shrubland environment (total of 273 DTMs and CHMs). We conclude that methodological effects have considerable potential to negatively affect replicability. A power-law relationship between point cloud density and dense matching resolution suggested that important dense matching resolution thresholds exist beyond which replicability degrades considerably. For our Arctic study area, replicability of DTMs (median ± 0.1 m RMSE Vegetated Vertical Accuracy) and CHMs (within ±0.05 m of true site-level heights) is most likely when source imagery is collected with ≤1.5 cm spatial resolution and side-lap of >80%, and if classified point clouds are generated using full-scale dense matching and Triangular Irregular Network filtering. Negative biases for maximum shrub height estimates increased from 4–9% to 14–50% with coarser imagery. We advocate for increased attention to drone-derived model replicability to separate real environmental changes from noise during a period of rapid ecological and geomorphic change. Full article

This paper presents an adaptive virtual oscillator control in coordination with an adaptive filter to mitigate subsynchronous oscillations in grid-forming converters caused by series compensation. Although series compensation enhances power transfer capability and transient stability margins, it can introduce subsynchronous resonance, leading to subsynchronous oscillations. Virtual oscillator control fed with set points is made dispatchable for grid-forming control to ensure the power-sharing, fast-synchronization, and subsynchronous oscillation damping capability of inverters. In this paper, taking advantage of power reserves in grid-forming operation, virtual oscillator control law is modified to dynamically change the set power point during low-resonance conditions to mitigate subsynchronous oscillations. Moreover, to overcome the limited damping capability of adaptive VOC during severe-resonance conditions, a coordinated adaptive adjustment of the grid-side filter inductance based on the modified power set point is designed. The IEEE’s first benchmark model is altered by integration with a 1000 MW grid-forming inverter in a MATLAB R2024b/Simulink environment. The previously proposed dispatchable virtual oscillator control and electronic-based FACT device, i.e., thyristor-controlled series capacitor, are implemented and analyzed under the same test system for the sake of comparison with the designed coordinated strategy. The simulation results are investigated in the time domain and frequency domain, and by calculating performance indices to verify the effectiveness of the proposed scheme. The overall analysis justifies the mitigated, low transient overshoot and high power quality of subsynchronous oscillations by using the designed strategy with varying compensation levels. Full article

The paper proposes a new hybrid sliding mode control algorithm based on saturated-type reaching law for current regulation of a grid-following inverter in a microgrid connected to the power grid, ensuring system stability under severe main grid voltage disturbances. The system contains the control system, T-type inverter, LCL filter, and DC source. First, a mathematical model of the above-described microgrid structure is proposed. The designs of well-known SMC algorithms used to control the power grid current are presented. This work introduces a new hybrid SMC method based on saturated-type reaching law, which is later used in the control system for a specific test scenario including voltage grid disturbance. For this case, an additional and extended stability analysis is conducted to obtain the controller parameters that shall provide the system with greater robustness and a faster convergence to the desired state after prior displacement. The primary objective of this study is to enhance the quality of transmitted energy in a power electronic system by means of a novel sliding mode control approach with a hybrid reaching law, while reducing the system’s sensitivity to selected external disturbances. Full article

Financial time series often show periods during which market index values or asset prices increase or decrease monotonically. These events are known as price runs, uninterrupted trends, or simply runs. By identifying such runs in the daily DJIA and IPC indices from 2 January 1990 to 17 October 2025, we construct their associated returns to obtain a non-arbitrary sample of multi-scale returns, which we call trend returns (TReturns). The timescale of each multi-scale return is determined by the exponentially distributed duration of its corresponding run. We empirically show that the distribution of these coarse-grained returns exhibits distinctive statistical properties: the central region displays an exponential decay, likely resulting from the exponential distribution of trend durations, while the tails follow a power-law decay. This combination of exponential central behavior and asymptotic power-law decay has also been observed in other complex systems, and our findings provide additional evidence of its natural emergence. We also explore the informational properties of multi-scale returns using three measures: Shannon entropy, permutation entropy, and compression-based complexity. We find that Shannon entropy increases with coarse-graining, indicating a wider range of values; permutation entropy drops sharply, revealing underlying temporal patterns; and compression ratios improve, reflecting suppressed randomness. Overall, these findings suggest that constructing TReturns filters out microscopic noise, reveals structured temporal patterns, and provides a complementary and clear view of market behavior. Full article

The integration of electric railways with renewable energy sources is crucial for advancing sustainable transportation and building clean, low-carbon, and efficient energy systems in alignment with global sustainable development goals. However, the application of photovoltaic (PV) integration into railway traction power supply systems may exacerbate resonance phenomena between electric locomotives and the traction network. It is therefore necessary to study the impedance frequency characteristics (IFCs) of traction networks to minimize harmonic resonance overvoltage. In this paper, a harmonic impedance model of the sustainable traction power supply system (STPSS) is established, and an impedance analysis method is adopted to reveal the influence law of grid-connected PV inverters on the IFCs of STPSSs. Additionally, to improve the stability of STPSSs, a multi-parameter co-tuning method based on an improved particle swarm optimization algorithm is proposed. This method constructs a multi-objective function that includes resonance frequency, impedance magnitude, and filtering cost, thereby realizing the automatic optimization of the control parameters and filtering parameters of PV inverters. The results demonstrate a 56% reduction in the maximum impedance magnitude within the 0–5 kHz frequency range and a 10.8% cost reduction in the LCL filter implementation, confirming the effectiveness of the proposed optimization model. Results show that the maximum impedance magnitude of the optimized system in the frequency range of 0–5 kHz can be reduced by 56%. Moreover, the cost of LCL filters can be reduced by 10.8% through component value optimization. These findings validate the effectiveness of the proposed method. Full article

High-performance tracking control for the hydraulic manipulator should address the challenges of the uncertainties and unknowns associated with the electro-hydraulic servo system (EHSS). This paper presents an extended state observer-based chattering-free terminal sliding-mode (ESO-CFTSM) control scheme for hydraulic manipulators. A third-order integral chain model is developed to characterize the system dynamics, where uncertainties and unknowns are considered as disturbances and estimated by the ESO. Meanwhile, a full-order TSM manifold is designed to stabilize the closed-loop system in finite-time. For this proposed scheme, the feedforward compensation of disturbances is introduced in the equivalent control law. Furthermore, the composite reaching law and a low-pass filter are used to realize the chattering-free control. The singularity is avoided because there are no derivatives of terms with fractional powers in the control law. The stability of the overall system is proved by Lyapunov technique. The simulations using the physical model of a hydraulic manipulator with coupled dynamics show the effectiveness of the proposed scheme for trajectory tracking problems. Simulation results indicate that the proposed ESO-CFTSM can achieve superior performance without being affected by lumped disturbances. Full article

Understanding the diversity of star formation histories (SFHs) of galaxies is key to reconstructing their evolutionary paths. Traditional models often assume parametric forms such as delayed-$\tau$ or exponentially declining models, which may not reflect the actual variety of formation processes. We aim to assess what types of SFHs are consistent with the observed present-day star formation rates (${\mathrm{SFR}}_0$) and time-averaged star formation rates ($⟨\mathrm{SFR}⟩$) of galaxies in the Local Volume, without assuming any fixed functional form. We construct a non-parametric framework by generating large ensembles of randomized SFHs for each galaxy in the sample. For each SFH, we compute its predicted stellar mass and present-day SFR and retain only those consistent with the observed values within a 20% tolerance. We then infer the statistical distribution of power-law slopes $\eta$ (fitted as $\mathrm{SFR}\left(t\right)\propto {(t-t_{\mathrm{start}})}^{\eta }$) and 50% stellar mass formation times $t_{50}$. Across the full sample of 555 galaxies, we find that ≈70% have flat SFHs ($\left|\eta \right|\le 0.01$), ≈24% are mildly declining ($\eta <-0.01$), and ≈6% are rising ($\eta >0.01$). In the low-mass bin ($M_{★}<3\times {10}^9{M}_{\odot }$), rising SFHs slightly increase (≈7%) but remain a minority as the majority have flat SFHs. Both $\eta$ and $t_{50}$ correlate strongly with the SFR ratio (Spearman $\rho >0.75$, $p\ll {10}^{-16}$), indicating that the shape and timing of star formation are primarily governed by this ratio. The $t_{50}$ distribution shows sharp spikes near 7.74 and 7.86 Gyr, which we attribute to grid discretization combined with filtering, rather than a physical bimodality. Our results confirm that strongly declining SFH templates are disfavored in the Local Volume: most systems are consistent with flat long-term SFHs, with only mild decline or occasional rising. Importantly, this is demonstrated through a fully model-independent, data-driven approach, with per-galaxy uncertainties quantified using the standard error of $\eta$ and $t_{50}$ from the ensemble of accepted SFHs. Full article

Exploring homogeneous dense groups is one of the important issues in social network structure measurement. k-core decomposition and core member filtering are common methods to uncover homogeneous dense groups in a network. However, existing methods of k-core decomposition struggle to support in-depth exploration of homogeneous dense groups. To address this issue, we store social networks in a graph database, taking advantage of its characteristics such as property indexes and batch queries. Based on this storage, we propose a k-core decomposition algorithm to improve the efficiency of homogeneous dense group detection. Subsequently, we introduce a core member filtering algorithm for identifying core members, a key exploration goal of this study. In experiments, we verify the efficiency of the k-core decomposition algorithm. Finally, we conduct an in-depth analysis of the characteristics of k-cores and their core members, yielding several important conclusions. For example, the relationship between the core number and the number of nodes obeys the power law distribution. In addition, we find that despite the strong connection of the core members, they do not play an important role in the information spreading of social networks. Full article