Search Results (1,214)

Fractional-order models provide a powerful framework for capturing memory-dependent and viscoelastic dynamics in mechanical systems, which are often inadequately represented by classical integer-order characterizations. This study addresses the identification of dynamic parameters in both single-degree-of-freedom (1-DOF) and three-degree-of-freedom (3-DOF) Duffing oscillators with fractional damping, modeled using the Grünwald–Letnikov characterization. The 1-DOF system includes a cubic nonlinear restoring force and is excited by a harmonic input to induce steady-state oscillations. For both systems, time domain simulations are conducted to capture long-term responses, followed by Fourier decomposition to extract steady-state displacement, velocity, and acceleration signals. These components are combined with a GL-based fractional derivative approximation to construct structured regressor matrices. System parameters—including mass, stiffness, damping, and fractional-order effects—are then estimated using pseudoinverse techniques. The identified models are validated through a comparison of reconstructed and original trajectories in the phase space, demonstrating high accuracy in capturing the underlying dynamics. The proposed framework provides a consistent and interpretable approach for frequency domain system identification in fractional-order nonlinear systems, with relevance to applications such as mechanical vibration analysis, structural health monitoring, and smart material modeling. Full article

Sepsis is a life-threatening syndrome caused by a dysregulated host response to infection. It follows a dynamic course in which early hyperinflammation coexists and overlaps with progressive immune suppression, a process best described as immunodynamic disruption. Key mechanisms include extensive lymphocyte death, expansion of regulatory T cells, impaired antigen presentation, and persistent activation of inhibitory checkpoints such as programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte–associated protein 4 (CTLA-4). These changes reduce immune competence and increase vulnerability to secondary infections. Clinically, reduced expression of Human Leukocyte Antigen–DR (HLA-DR) on monocytes and persistent lymphopenia have emerged as robust biomarkers for patient stratification and timing of immunomodulatory therapies. Beyond the acute phase, many survivors do not achieve full immune recovery but instead develop a Persistent Immune Remnant, defined as long-lasting immune, metabolic, and endothelial dysfunction despite apparent clinical resolution. Recognizing PIR emphasizes the need for long-term monitoring and biomarker-guided interventions to restore immune balance. To integrate these observations, we propose the SIMMP–Sepsis model (Sepsis-Associated Persistent Multiorgan Immunometabolic Syndrome), which links molecular dysfunction to clinical trajectories and provides a framework for developing precision immunotherapies. This perspective reframes sepsis not only as an acute crisis but also as a chronic immunometabolic syndrome, where survival marks the beginning of active immune restoration. Full article

This study experimentally investigates the wake-induced vibration (WIV) behavior of a bio-inspired harbor seal whisker model subjected to upstream propeller-generated unsteady flows. Vibration amplitudes, frequencies, and wake–whisker interactions were systematically evaluated under various flow conditions. The test matrix included propeller rotational speed N_p = 0~5000 r/min, propeller diameter D_p = 60~100 mm, incoming flow velocity U_∞ = 0~0.2 m/s, and separation distance between the whisker model and the propeller L/D = 10~30 (D = 16 mm, diameter of the whisker model). Results show that inline (IL) and crossflow (CF) vibration amplitudes increase significantly with propeller speed and decrease with increasing separation distance. Under combined inflow and wake excitation, non-monotonic trends emerge. Frequency analysis reveals transitions from periodic to subharmonic and broadband responses, depending on wake structure and coherence. A non-dimensional surface fit using L/D and the advance ratio (J = U_∞/(N_pD_p)) yielded predictive equations for RMS responses with good accuracy. Phase trajectory analysis further distinguishes stable oscillations from chaotic-like dynamics, highlighting changes in system stability. These findings offer new insight into WIV mechanisms and provide a foundation for biomimetic flow sensing and underwater tracking applications. Full article

This paper proposes an optimal-sliding-mode-based adaptive dynamic programming (ADP) master–slave synchronous control strategy for the actuator saturation and performance constraints that AUVs face in dynamic recovery. First, by introducing the sliding-mode function into the value function to optimize the state error and its derivative simultaneously, the convergence speed is significantly improved. Second, by designing the performance constraint function to directly map the sliding-mode function, the evolution trajectory of the sliding-mode function is constrained, ensuring the steady-state and transient characteristics. In addition, the hyperbolic tangent function (tanh) is introduced into the value function to project the control inputs into an unconstrained policy domain, thereby eliminating the phase lag inherent in conventional saturation compensation schemes. Finally, the requirement for initial stability is relaxed by constructing a single-critic network to approximate the optimal control policy. The simulation results show that the proposed method has significant advantages in terms of the position and attitude synchronization error convergence rate, steady-state accuracy, and control signal continuity compared with the conventional ADP method. Full article

Empirical research on the impact of the digital economy on sustainable development is hampered by severe methodological challenges. Discrepancies in the theoretical foundations and construction logic of measurement frameworks have led to diverse and often conflicting conclusions, hindering the systematic accumulation of knowledge. This study aims to address this critical gap by proposing a new, logically consistent measurement framework. To overcome the existing limitations, we construct a functional deconstruction framework grounded in General-Purpose Technology (GPT) theory and a “stock–flow” perspective. This framework deconstructs the digital economy into a neutral “digital infrastructure” (stock platform) and two forces reflecting its inherent duality: a “consumption force” (digital industrialization) and an “empowerment force” (industrial digitalization). Based on this, we develop a measurement system adhering to the principle of “logical purity” and apply a “two-step entropy weighting method with annual standardization” to assess 30 provinces in China from 2012 to 2023. Our analysis reveals a multi-scalar evolution. At the micro level, we identified four distinct provincial development models and three evolutionary paths. At the macro level, we found that the overall inter-provincial disparity followed an inverted U-shaped trajectory, with the core contradiction shifting from an “access gap” to a more profound “application gap.” Furthermore, the primary driver of this disparity has transitioned from being “empowerment-led” to a new phase of a “dual-force rebalancing.” The main contribution of this study is the provision of a new analytical tool that enables a paradigm shift from “aggregate assessment” to “structural diagnosis.” By deconstructing the digital economy, our framework allows for the identification of internal structural imbalances and provides a more robust and nuanced foundation for future causal inference studies and evidence-based policymaking in the field of digital sustainability Full article

Driven by both natural and anthropogenic factors, farmland abandonment and recultivation constitute complex and widespread global phenomena that impact the ecological environment and society. In the Inner Mongolia Yellow River Basin (IMYRB), a critical tension lies between agricultural production and ecological conservation, characterized by dynamic bidirectional transitions that hold significant implications for the harmony of human–nature relations and the advancement of ecological civilization. With the development of remote sensing, it has become possible to rapidly and accurately extract farmland changes and monitor its vegetation restoration status. However, mapping abandoned farmland presents significant challenges due to its scattered and heterogeneous distribution across diverse landscapes. Furthermore, subjectivity in questionnaire-based data collection compromises the precision of farmland abandonment monitoring. This study aims to extract crop phenological metrics, map farmland abandonment, and recultivation dynamics in the IMYRB and assess post-transition vegetation changes. We used Landsat time-series data to detect the land-use changes and vegetation responses in the IMYRB. The Farmland Abandonment and Recultivation Extraction Index (FAREI) was developed using crop phenology spectral features. Key crop-specific phenological indicators, including sprout, peak, and wilting stages, were extracted from annual MODIS NDVI data for 2020. Based on these key nodes, the Landsat data from 1999 to 2022 was employed to map farmland abandonment and recultivation. Vegetation recovery trajectories were further analyzed by the Mann–Kendall test and the Theil–Sen estimator. The results showed rewarding accuracy for farmland conversion mapping, with overall precision exceeding 79%. Driven by ecological restoration programs, rural labor migration, and soil salinization, two distinct phases of farmland abandonment were identified, 87.9 kha during 2002–2004 and 105.14 kha during 2016–2019, representing an approximate 19.6% increase. Additionally, the post-2016 surge in farmland recultivation was primarily linked to national food security policies and localized soil amelioration initiatives. Vegetation restoration trends indicate significant greening over the past two decades, with particularly significant increases observed between 2011 and 2022. In the future, more attention should be paid to the trade-off between ecological protection and food security. Overall, this study developed a novel method for monitoring farmland dynamics, offering critical insights to inform adaptive ecosystem management and advance ecological conservation and sustainable development in ecologically fragile semi-arid regions. Full article

A multiphase model of hydrothermal liquefaction (HTL) using the computational fluid dynamics coupling discrete element method (CFD-DEM) is used to simulate biocrude oil production from sludge flocs in a continuous stirred tank reactor (CSTR). Additionally, the influence of the agitator speed and the slurry flow rate on dynamic biocrude oil production is investigated through full transient CFD analysis in a scaled-up CSTR study. The kinetics of the HTL mechanism as a function of temperature, pressure, and residence time distribution were employed in the model through a user-defined function (UDF). The multiphysics simulation of the HTL process in a stirred tank reactor using the Lagrangian–Eulerian (LE) approach, along with a standard k-ε turbulence model, integrated HTL kinetics. The simulation accounts for particle–fluid interactions by coupling CFD-derived hydrodynamic fields with discrete particle motion, enabling prediction of individual particle trajectories based on drag, buoyancy, and interphase momentum exchange. The three-phase flow using a compressible non-ideal gas model and multiphase interaction as design requirements increased process efficiency in high-pressure and high-temperature model conditions. Full article

This paper systematically examines the key developmental stages of China’s new energy vehicle (NEV) charging and battery swapping industry, analyzing technological breakthroughs, market expansion, and policy support in each phase. The study identifies three distinct stages: the initial exploration phase (before 2014), the comprehensive deployment phase (2014–2020), and the high-quality development phase (since 2021). The industry has established a diverse energy replenishment system centered on charging infrastructure, with battery swapping serving as a complementary approach. Policy implementation has yielded significant achievements, including rapid infrastructure expansion, continuous technological upgrades, innovative business models, and improved user experiences. However, persistent challenges remain, such as insufficient standardization, unprofitable business models, and coordination barriers between stakeholders. The paper forecasts future development trajectories, including the widespread adoption of high-power charging technology, intelligent charging system upgrades, integration of Solar Power, Energy Storage, and EV Charging, diversified operational ecosystems for charging/swapping facilities, deep integration of virtual power plants, and the construction of comprehensive energy stations. Policy recommendations emphasize strengthening standardization, optimizing regional coordination and subsidy mechanisms, enhancing participation in virtual power plant frameworks, promoting the interoperability of charging/swapping infrastructure, and advancing environmental sustainability through resource recycling. Full article

Rapid regional development and intensified human activities increasingly disturb ecosystems, posing substantial threats to the integrity of large-scale ecological zones. As a World Natural Heritage site and a crucial national ecological zone, the Zhangjiajie Scenic Area plays a pivotal role in China’s environmental conservation efforts. To comprehensively assess tourism ecological security in the Scenic Area and strengthen the scientific basis for resource management and policymaking, this study developed a multi-dimensional ecological security evaluation system covering 2010–2024, incorporating dynamic changes in perturbation, reaction, and governance. Using entropy weight–TOPSIS and coupling coordination models, combined with obstacle degree analysis, we examined the temporal trajectory of ecological security and analyzed its underlying driving mechanisms. The study also examined factors influencing the sustainable development of the ecosystem. The results indicate the following: (1) Tourism ecological security in the Scenic Area followed a V-shaped trajectory of “rapid degradation—steady recovery—impact and rebound.” It declined sharply to an unsafe level between 2010 and 2014, steadily recovered from 2015 to 2019, briefly dropped in 2020, and then rebounded, reaching a peak evaluation value of 0.519 in 2024. (2) The co-evolution of perturbation, reaction, and governance subsystems has matured: their coupling coordination degree has increased annually and has remained at the level of “intermediate coordination” since 2020. The reaction subsystem plays a central role, serving as a bridge between perturbation and governance. (3) The driving factors exhibit a phased evolutionary pattern of “elements—facilities—structure—function.” Cultivated land area, total road mileage, and artificial afforestation area constitute the main long-term constraints. This research provides important insights for strengthening ecological security and sustainability in the Scenic Area while advancing regional ecosystem development. It also offers valuable guidance for ecological security management and policymaking in similar nature reserves. Full article

Focusing on joint-space time-optimal trajectory planning for industrial robots, this study integrates 3-5-3 piecewise polynomial parameterization with an improved Fire Hawk Optimization algorithm (TFHO). Subject to joint position, velocity, and acceleration limits, segment durations are optimized as decision variables. TFHO employs Tent-chaotic initialization to improve the uniformity of initial solutions and a two-phase adaptive Lévy–Gaussian–Cauchy hybrid mutation to balance early global exploration with late local exploitation, mitigating premature convergence and enhancing stability. On benchmark functions, TFHO attains the lowest mean area under the convergence curve (AUC; lower is better). Wilcoxon signed-rank tests show statistically significant improvements over FHO, PSO, GWO, and WOA $(p\le 0.05)$. Ablation studies indicate a pronounced reduction in run-to-run variability: the standard deviation decreases from 0.3157 (FHO) to 0.0023 with TFHO, a 99.27% drop. In an ABB IRB-2600 simulation case, the execution time is shortened from 12.00 s to 9.88 s (−17.66%) while preserving smooth and continuous kinematic profiles (position, velocity, and acceleration), demonstrating practical engineering applicability. Full article

Clarifying the changes in the flow field, trajectory, and range of particulate matter such as input detritus and feces of marine aquaculture in offshore wind farms is of great importance for optimizing the layout of cage culture, preventing water pollution, and promoting the integrated development of wind power and aquaculture. This study designed multiple scenarios based on the basic data of the Nanpeng Island wind farm. The flow field changes were simulated through a k-epsilon model based on the porous medium model, and the particle diffusion range and trajectory were simulated via the discrete phase model (DPM) and the MIKE 21 model. The results showed that flow velocities in the whole area, except in the region near the wind turbine, were unaffected by the monopile or jacket foundation. The center velocities of the cages decreased by 14.58% and 21.45%, respectively, when culture density increased from 12.5 to 20 kg/m³. In the case of one-way inflow, placing rafts upstream of the aquaculture area can effectively slow down the flow velocity, which is reduced by 45.2% and 32.3% at the inlet and center of the cage, respectively. In the case of the occurrence of unidirectional water flow, downstream raft frames, arranged in a triangular pattern, could align with the cage center axis. Under actual sea conditions, the raft frame could be arranged in an elliptical shape around the cage. The ratio of the length of its major axis to that of its minor axis is approximately 3:1. Full article

Electromagnetic implosions of hollow lithium cylinders can be utilized to compress magnetized plasma targets in the context of Magnetized Target Fusion (MTF). Two small-scale experiments were conducted at General Fusion as a stepping stone toward compressing magnetized plasmas on a larger scale. The first experiment is an electromagnetic implosion of a lithium ring, and the second is a compression of toroidal magnetic flux by imploding a hollow lithium cylinder onto an hourglass-shaped central structure. Here we present the methodology and results of modelling these experiments with OpenFOAM. Our in-house axisymmetric compressible MHD multi-phase solver was further extended to incorporate: (i) external RLC circuit model for electromagnetic compression coils and (ii) diffusion of the magnetic field into multiple solid materials. The implementation of the external RLC circuit model for electromagnetic coils was verified by comparison with results obtained with FEMM software and with the analytical solution. The solver was then applied to model both experiments and the main conclusions are as follows: (i) modelling solid lithium as a high-viscosity liquid is an adequate approach for the problems considered; (ii) the magnetic diffusivity of lithium is an important parameter for the accurate prediction of implosion trajectories (for the implosion of the lithium ring, higher values of magnetic diffusivity in the range $0.2 \le {\eta }_{\mathrm{ring}}[{\mathrm{m}}^2/\mathrm{s}] \le 0.5$ resulted in a better fit to the experimental data with a relative deviation in the trajectory of $\lesssim 20\%$); (iii) simulation results agree well with experimental data, and in particular, the toroidal field amplification of 2.25 observed in the experiment is reproduced in simulations within a relative error margin of 20%. The solver is proven to be robust and has the potential to be employed in a variety of applications. Full article

Spilled hydrocarbons released from oil pipeline accidents can result in long-term environmental contamination and significant damage to habitats. In this regard, evaluating actions in response to vulnerability scenarios is fundamental to emergency management and groundwater integrity. To this end, understanding the trajectories and their influence on the various parameters and characteristics of the contaminant’s fate through accurate numerical simulations can aid in developing a rapid remediation strategy. This paper develops a numerical model using the CactusHydro code, which is based on a high-resolution shock-capturing (HRSC) conservative method that accurately follows sharp discontinuities and temporal dynamics for a three-phase fluid flow. We analyze nine different emergency scenarios that represent the breaking of a diesel oil onshore pipeline in a porous medium. These scenarios encompass conditions such as dry season rupture, rainfall-induced saturation, and varying pipeline failure pressures. The influence of the spilled oil pressure and water saturation in the unsaturated zone is analyzed by following the saturation contour profiles of the three-phase fluid flow. We follow with the high-accuracy formation of shock fronts of the advective part of the migration. Additionally, the mass distribution of the expelled contaminant along the porous medium during the emergency is analyzed and quantified for the various scenarios. The results obtained indicate that the aquifer contamination strongly depends on the pressure outflow in the vertical flow. For a fixed pressure value, as water saturation increases, the mass of contaminant decreases, while the contamination speed increases, allowing the contaminant to reach extended areas. This study suggests that, even for LNAPLs, the distribution of leaked oil depends strongly on the spill pressure. If the pressure reaches 20 atm at the time of pipeline failure, then contamination may extend as deep as two meters below the water table. Additionally, different seasonal conditions can influence the spread of contaminants. This insight could directly inform guidelines and remediation measures for spill accidents. The CactusHydro code is a valuable tool for such applications. Full article

Utilizing a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach, this study constructs a comprehensive three-dimensional numerical model to simulate particle migration dynamics within rough artificial fractures subjected to the high-energy impact of water inrush. The model explicitly incorporates key governing factors, including intricate fracture wall geometry characterized by the joint roughness coefficient (JRC) and aperture variation, hydraulic pressure gradients representative of inrush events, and polydisperse sand particle sizes. Sophisticated simulations track the complete mobilization, subsequent acceleration, and sustained transport of sand particles driven by the powerful high-pressure flow. The results demonstrate that particle migration trajectories undergo a distinct three-phase kinetic evolution: initial acceleration, intermediate coordination, and final attenuation. This evolution is critically governed by the complex interplay of hydrodynamic shear stress exerted by the fluid flow, frictional resistance at the fracture walls, and dynamic interactions (collisions, contacts) between individual particles. Sensitivity analyses reveal that parameters like fracture roughness exert significant nonlinear control on transport efficiency, with an identified optimal JRC range (14–16) promoting the most effective particle transit. Hydraulic pressure and mean aperture size also exhibit strong, nonlinear regulatory influences. Particle transport manifests through characteristic collective migration patterns, including “overall bulk progression”, processes of “fragmentation followed by reaggregation”, and distinctive “center-stretch-edge-retention” formation. Simultaneously, specific behaviors for individual particles are categorized as navigating the “main shear channel”, experiencing “boundary-disturbance drift”, or becoming trapped as “wall-adhered obstructed” particles. Crucially, a robust multivariate regression model is formulated, integrating these key parameter effects, to quantitatively predict the critical migration time required for 80% of the total particle mass to transit the fracture. This investigation provides fundamental mechanistic insights into the particle–fluid dynamics underpinning hazardous water–sand inrush phenomena, offering valuable theoretical underpinnings for risk assessment and mitigation strategies in deep underground engineering operations. Full article

Forest disturbance is a major driver shaping the structure and function of plantation ecosystems. Current research predominantly focuses on single forest types or landscape scales. However, species-level fine-scale assessments of disturbance dynamics are still scarce. In this study, we investigated Chinese fir (Cunninghamia lanceolata), Armand pine (Pinus armandii), and Yunnan pine (Pinus yunnanensis) plantations in the mountainous eastern Yunnan Plateau. We developed a Spatial Coupling Framework of Disturbance Legacy (SC-DL) to systematically elucidate the spatial associations between contemporary species distribution patterns and historical disturbance regimes. Using the Google Earth Engine (GEE) platform, we reconstructed pixel-level disturbance trajectories by integrating long-term Landsat time series (1993–2024) and applying the LandTrendr algorithm. By fusing multi-source remote sensing features (Sentinel-1/2) with terrain factors, employing RFE, and performing a multi-model comparison, we generated 10 m-resolution species distribution maps for 2024. Spatial overlay analysis quantified the cumulative proportion of the historically disturbed area and the spatial aggregation patterns of historical disturbances within current species ranges. Key results include the following: (1) The model predicting disturbance year achieved high accuracy (R² = 0.95, RMSE = 2.02 years, MAE = 1.15 years). The total disturbed area from 1993 to 2024 was 872.7 km², exhibiting three distinct phases. (2) The random forest (RF) model outperformed other classifiers, achieving an overall accuracy (OA) of 95.17% and a Kappa coefficient (K) of 0.93. Elevation was identified as the most discriminative feature. (3) Significant spatial differentiation in disturbance types emerged: anthropogenic disturbances (e.g., logging and reforestation/afforestation) dominated (63.1% of total disturbed area), primarily concentrated within Chinese fir zones (constituting 70.2% of disturbances within this species’ range). Natural disturbances accounted for 36.9% of the total, with fire dominating within the Yunnan pine range (79.3% of natural disturbances in this zone) and drought prevailing in the Armand pine range (71.3% of natural disturbances in this zone). (4) Cumulative disturbance characteristics differed markedly among species zones: Chinese fir zones exhibited the highest cumulative proportion of disturbed area (42.6%), with strong spatial aggregation. Yunnan pine zones followed (36.5%), exhibiting disturbances linearly distributed along dry–hot valleys. Armand pine zones showed the lowest proportion (20.9%), characterized by sparse disturbances within fragmented, high-altitude habitats. These spatial patterns reflect the combined controls of topographic adaptation, management intensity, and environmental stress. Our findings establish a scientific basis for identifying disturbance-prone areas and inform the development of differentiated precision management strategies for plantations. Full article