Search Results (4,378)

The driven damped pendulum is a foundational model in nonlinear dynamics, with applications ranging from Josephson junctions to MEMS oscillators. Conventional dimensionless treatments obscure the common physical origin of damping and driving in the inertia coefficient. Here we restore this dependence and establish inertia as a master control parameter governing stability, resonance, and bifurcations. Through linear analysis and perturbation theory, we derive universal scaling laws revealing a fundamental dichotomy: quantities at resonance—peak amplitude and nonlinear frequency shift—are independent of inertia due to exact algebraic cancellation between the inertia dependence of the effective driving amplitude and effective damping coefficient. Off resonance, however, amplitude scales inversely with inertia, bandwidth narrows proportionally, and the bistability threshold exhibits an even steeper dependence. A critical inertia separates underdamped from overdamped regimes, yielding non-monotonic relaxation times that maximize attractor memory at extreme inertia values. These scaling laws provide design guidelines: low inertia promotes broadband response for energy harvesting; high inertia suppresses off-resonant vibrations for precision timing and quantum applications. By establishing inertia as a physically realizable path through parameter space, this work unifies disparate phenomena and provides a framework for understanding stability in inertial-driven systems. Full article

This paper presents a practical optimization framework for improving trajectory feasibility in constrained nonlinear optimal control problems for agile unmanned aerial vehicles (UAVs). The proposed method addresses trajectory optimization problems with non-convex geometric constraints, where gradient-based solvers often fail to converge to feasible solutions. The framework combines Model Predictive Path Integral (MPPI) control and the Augmented Lagrangian iterative Linear Quadratic Regulator (AL-iLQR). MPPI is employed as a fast sampling-based guidance mechanism to explore feasible regions of the trajectory space, while AL-iLQR is used to efficiently refine locally optimal solutions with high numerical accuracy. By decoupling feasibility exploration from local optimal refinement, the proposed method mitigates the sensitivity of gradient-based trajectory optimization to initialization in highly constrained environments. Numerical simulations involving both simplified two-dimensional dynamics and full quadrotor models demonstrate that the proposed approach significantly improves the probability of converging to feasible and dynamically consistent trajectories compared with AL-iLQR alone. The proposed method does not aim to provide theoretical guarantees of global optimality; instead, it offers a practical and computationally efficient strategy for enhancing feasibility and robustness in real-time UAV trajectory optimization. Full article

Climate hazards increasingly disrupt schooling, revealing the limits of preparedness models that treat teachers only as implementers. This study reframes teacher empowerment as a climate-resilience capability and examines how governance arrangements enable (or constrain) hazard-ready education systems. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), searches of Web of Science, Scopus, and Google Scholar (2000–2025) identified 53 eligible studies. Across diverse hazards and settings, the evidence converges on a governance-to-capability pathway: empowerment becomes resilient performance only when the delegated decision space is matched with financed capacity (time, training, contingency resources), timely risk information and functional communication/digital infrastructure, institutionalized cross-sector coordination (education–DRR–health–protection–local government), and learning-oriented accountability (after-action review and adaptive revision rather than punitive compliance). Reported outcomes include higher preparedness quality, earlier protective action, improved learning continuity and safeguarding, and more sustainable teacher well-being/retention. Predictable failure modes include mandate–resource mismatch, accountability overload, unstable centralization–autonomy dynamics, and inequitable empowerment distribution affecting rural schools, women, and contract teachers, and disability inclusion. The evidence gaps remain pronounced for chronic hazards (especially heat and wildfire smoke), high-vulnerability contexts (fragile/conflict settings and informal settlements), and standardized measures of equity, burden distribution, governance performance, and cost-effectiveness. Policies should prioritize integrated governance packages with explicit protection and equity safeguards. Full article

The exact response theory, also known as Transient Time Correlation Function formalism, is a powerful method concerning how observables respond to a given perturbation of the dynamics of the systems of interest, and it extends linear response theory to generic (autonomous) dynamical systems. Its main ingredient is the so-called dissipation function. In this paper, we adapt this theory for time-lagged systems, and we illustrate its applicability considering simple examples of delay equations, with different memory terms. Adopting the technique already used for time deterministic as well as stochastic time-dependent perturbations, the dynamics is described in a higher dimensional phase space, in which the delay-dependent dynamics is mapped into an augmented phase space: the new dynamics is proven to be autonomous and suitable for the exact responses to be computed. In addition, we explore the comparison between linear and exact approaches for a specific kernel choice. Full article

The quantum Mpemba effect (QME) describes the counterintuitive phenomenon where a system initially further from equilibrium relaxes faster than one closer to it. Specifically, the QME associated with symmetry restoration has been extensively investigated across integrable, ergodic, and disordered localized systems. However, its fate in disorder-free ergodicity-breaking settings, such as the Stark many-body localized (Stark-MBL) phase, remains an open question. Here, we explore the dynamics of local $U\left(1\right)$ symmetry restoration in a Stark-MBL XXZ spin-$\frac{1}{2}$ chain, using the Rényi-2 entanglement asymmetry (EA) as a probe. Using an analytical operator-string expansion supported by numerical simulations, we demonstrate that the QME transitions from an initial-state-dependent anomaly in the ergodic phase to a universal feature in the Stark-MBL regime. Moreover, the Mpemba time scales exponentially with the subsystem size, even in the absence of global transport, and is governed by high-order off-resonant processes. We attribute this robust inversion to a Stark-induced hierarchy of relaxation channels that fundamentally constrains the effective Hilbert space dimension. The findings pave the way for utilizing tunable potentials to engineer and control anomalous relaxation timescales in quantum technologies without reliance on quenched disorder. Full article

Autonomous exploration in unknown environments remains a challenging problem for UAVs. This paper proposes a hierarchical exploration planning framework that explicitly leverages real-time acquired prior knowledge to improve exploration efficiency. To efficiently represent the structural information embedded in the prior knowledge, two map structures, namely the quasi-prior map and the hybrid-topo map, are designed, enabling more reasonable space partition and facilitating exploration planning. Subsequently, based on the hybrid-topo map, the hierarchical exploration planner computes a global exploration guidance that provides an efficient traversal order over all unexplored regions. The local coverage problem in unknown regions is formulated as a coverage traveling salesman problem (CTSP), where visibility information derived from the hybrid-topo map is exploited to optimize local viewpoint sequences with high coverage efficiency. Finally, a long-horizon trajectory planning strategy is proposed to maintain high flight speed while ensuring safety and dynamic feasibility. Simulations demonstrate that the proposed framework significantly outperforms state-of-the-art exploration methods in terms of exploration efficiency, while ablation studies further validate the effectiveness of each module. Real-world experiments are conducted to confirm the practical capability of the proposed approach. Full article

Urban green spaces (UGS) are critical regulators of carbon sequestration in industrial cities; however, the configuration mechanisms underlying their carbon dynamics remain insufficiently understood. This study investigates how landscape configuration influences carbon sequestration capacity in Lanzhou and Baotou using multi-temporal datasets from 2000, 2011, and 2022. Net primary productivity (NPP) derived from the CASA model was employed to represent carbon sequestration capacity. An integrated XGBoost-SHAP framework was applied to identify dominant configuration metrics, nonlinear responses, and structural thresholds. The XGBoost model showed stable predictive performance across the three periods, with test-set R² values ranging from 0.470 to 0.510 in Lanzhou and from 0.325 to 0.379 in Baotou. The results reveal systematic and persistent differences in configuration-driven controls between the two cities. In Lanzhou, aggregation-related metrics, particularly COHESION, consistently exert the strongest influence across all three periods, indicating that spatial cohesion and connectivity function as primary stabilizing mechanisms in a mountainous, valley-constrained urban system. Carbon sequestration performance increases once sufficient structural integration is achieved, with aggregation thresholds remaining relatively stable, for example AI values of approximately 0.31–0.34 across 2000–2022, reflecting the importance of maintaining ecological continuity under semi-arid climatic stress. In contrast, Baotou is more strongly regulated by fragmentation-related metrics, especially edge density (ED) and division index (DIVISION), suggesting that its relatively open terrain and industrial spatial structure render carbon sequestration more sensitive to patch separation and edge proliferation. Here, fragmentation acts as a dominant structural constraint, limiting vegetation productivity once spatial disintegration intensifies; for example, ED thresholds shifted from approximately −0.23 in 2000 to −0.56 in 2022. Landscape–carbon relationships exhibit pronounced nonlinear and threshold-dependent behavior in both cities. Rather than responding gradually to structural modification, NPP shifts across identifiable transition points that remain broadly stable over time; for instance, Lanzhou’s AI threshold remains within 0.31–0.34, whereas Baotou’s ED threshold changes from −0.23 to −0.56 across 2000–2022, indicating that these thresholds represent intrinsic structural characteristics of the respective urban ecological systems. However, the magnitude and configuration logic of these thresholds differ between Lanzhou and Baotou, confirming the existence of city-specific nonlinear regimes. These findings demonstrate that urban carbon sequestration operates through context-dependent configuration pathways shaped by terrain, climatic constraints, and long-term spatial organization. The study advances understanding of how structural heterogeneity governs carbon dynamics in arid and semi-arid industrial cities and provides a quantitative basis for configuration-sensitive land planning. Full article

We present a geodesic dynamics framework for discrete-time state evolution on the unit sphere $S^{N-1}$ that maintains exact unit-norm constraints through Riemannian exponential mapping. Given an input sequence and an initial state, the method constructs trajectories by projecting inputs to tangent spaces and updating states along geodesics, incorporating temporal memory via approximate parallel transport of velocity directions. Unlike traditional approaches requiring post hoc normalization of linear updates, the geodesic formulation preserves $\parallel {\mathbf{x}}_t\parallel =1$ to machine precision while eliminating explicit $N\times N$ transition matrices in favor of $D\times N$ input embeddings when the intrinsic input dimension D is much smaller than the ambient dimension N. The update corresponds to a first-order exponential integrator on the sphere. We establish local Lipschitz continuity of the exponential map on positively curved manifolds with careful treatment of basepoint dependence, derive perturbation bounds showing linear-to-exponential growth transitions via Grönwall-type estimates, and we prove third-order asymptotic equivalence with normalized linear systems under appropriate scaling. Numerical experiments on synthetic data validate exact norm preservation over extended time horizons, confirm theoretical perturbation growth predictions, and demonstrate the effectiveness of the temporal memory mechanism in reducing long-horizon prediction errors. The framework provides a principled geometric approach for applications requiring exact directional or compositional constraints. Full article

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification. Full article

Sustainable urban development increasingly depends on understanding how digital activity is distributed across space and time, yet the spatiotemporal dynamics of the urban digital landscape remain poorly mapped by conventional data sources. This study uses Beijing as an empirical testbed, applying a multi-dimensional analytical framework to massive mobile network traffic data to decode the metabolic rhythms, distributional laws, and functional organization of the urban digital landscape. The results reveal three findings. First, the urban digital landscape exhibits a sleepless trapezoidal temporal rhythm characterized by continuous saturation without a midday trough and a quantifiable weekend activation lag, indicating that digital metabolism is structurally decoupled from physical mobility patterns. Second, digital traffic follows a skew-normal distribution consistent with a 20/70 rule of spatial polarization, in which the top 20% of super-connector nodes sustain approximately 70% of total urban digital flow, yielding a Gini coefficient of 0.68 as a measurable indicator of infrastructure inequality and systemic vulnerability. Third, four distinct functional prototypes are identified—ranging from continuously active metropolitan cores to inverse-tidal ecological peripheries—empirically validating Beijing’s polycentric transformation through the lens of digital flows. These findings demonstrate that large-scale mobile network traffic data offers a replicable and structurally distinct lens for sustainable urban digital governance, supporting resilient network planning, equitable allocation of digital resources, and evidence-based monitoring of urban functional transformation in rapidly growing megacities. Full article

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations. Full article

Chinese font generation is important for digital typography, cultural preservation, and personalized user interfaces. However, existing methods often face challenges in maintaining structural consistency, supporting diverse stylistic variations, and achieving computational efficiency simultaneously, especially in cloud-based environments. A key application is bandwidth-efficient font delivery, where compact structural templates replace large font files for on-demand style customization. To address these issues, this paper proposes Stroke2Font—a hierarchical vector model with AI-driven optimization for dynamic Chinese font generation. The core model decouples structural representation from style rendering through stroke element decomposition and Bézier curve parameterization. To further balance structural fidelity, style diversity, and real-time performance, we introduce a three-module optimization framework: (1) a reinforcement learning policy for dynamic selection of Bézier control parameters to minimize rendering latency; (2) a genetic algorithm for exploring style vector spaces and generating novel font variants; and (3) an adaptive complexity-aware optimization strategy that dynamically configures parameters based on character structural complexity. Experimental results on a dataset of 150 Chinese characters with 1123 stroke trajectories and 5287 feature points demonstrate that the adaptive complexity-aware optimization achieves the highest trajectory similarity of 65.2%, representing a 6.4% relative improvement over baseline (61.3%). The evaluation covers characters ranging from 1 to 18 strokes across 6 stroke types, with standard deviation reduced to ±5.7% (compared to ±6.5% baseline), indicating more consistent performance. Quantitative analysis confirms that the method generalizes effectively across varying character complexity, with the optimization showing stable improvement regardless of stroke count distribution. These results validate that Stroke2Font provides an effective solution for high-quality, efficient, and scalable Chinese font generation in cloud-based applications. Full article

In low-Earth-orbit (LEO) satellite networks, the need for intelligent parameter-adjustment strategies has become increasingly critical due to the presence of highly dynamic channel conditions, limited spectrum resources, and complex interference environments. In this paper, a method for optimizing LEO satellite communication links based on deep reinforcement learning (DRL) is proposed. Through the optimization of the transmit power, the modulation and coding scheme (MCS), the beamforming parameters, and the retransmission mechanisms, adaptive link control is achieved in dynamic operational scenarios. A multidimensional state space is constructed, within which the channel state information, the interference environment, and the historical performance metrics are integrated. The spatio-temporal characteristics of the channel are extracted by means of a hybrid neural architecture that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM) network. To effectively accommodate both continuous and discrete action spaces, a hybrid DRL framework that combines proximal policy optimization (PPO) with a deep Q-network (DQN) is employed, thereby enabling cross-layer optimization of the physical-layer and link-layer parameters. The results demonstrate that substantial improvements in throughput, bit error rate (BER), and transmit-power efficiency are achieved under severely time-varying channel conditions, which provides a new idea for resource management and dynamic-environment adaptation in satellite communication systems. Full article

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

People in midlife interact with several different environments during their daily life including employment, leisure, commuting, and various family responsibilities, a concept defined as activity space. However, little is known about how these activity spaces contribute to individuals’ daily health behavior choices. The Everyday Environments and Experiences (E3) study was conducted to explore these relationships. In this paper, we provide a reproducible GPS processing workflow to generate time-weighted exposure measures (activity spaces) inferred from 21 days of continuous GPS monitoring among 340 midlife adults in Cook County, Illinois (n = 340) from the E3 study. Data from waist-mounted GPS devices that recorded one-minute location epochs were aggregated after excluding time spent within an 800 m buffer around the home. For each epoch, we derived proximity and kernel density measures for eleven food and physical-activity-related location types (e.g., supermarkets, fitness facilities), along with twenty-six environmental context variables (e.g., land use, crime, population density). Time-weighted averages characterized each participant’s typical non-home environmental exposure. After adjustment for environmental context, age and gender were generally unrelated to activity-space measures. However, Black and Hispanic participants (as compared to White participants) spent less time near both food and physical-activity resources, suggesting systemic inequities in access beyond neighborhood composition. These findings highlight the need to move beyond static residential measures toward time-weighted, dynamic assessments of environmental exposure. They also indicate that racial and ethnic disparities in routine activity space may reflect structural inequities shaping daily physical activity and access to healthy food. Future research is needed to explore how these observed disparities translate into differences into disease risk, using longer exposure periods and different geographic settings to identify causal pathways and inform multi-level interventions. Full article