Search Results (1,744)

The N-body problem is a classic problem involving a system of N discrete bodies mutually interacting in a dynamical system. At any moment in time there are $N(N-1)/2$ such interactions occurring. This $N^2$ scaling leads to computational difficulties where simulations range from tens of thousands of bodies to billions or trillions. Approximation algorithms, such as the famous Barnes–Hut algorithm, simplify the number of interactions to scale as $NlogN$. Even still, this improvement in complexity is insufficient to achieve the desired performance for very large simulations on computing clusters with many nodes and many cores. In this work we explore a variety of algorithmic techniques for parallel and distributed variations on the Barnes–Hut algorithm to improve parallelism and reduce inter-process communication requirements. This includes the costzones and hashed octree techniques. We implement these techniques in a gravitational N-body simulation and show that they can be applied to both a parallel and distributed context. This work collects and unifies over 30 years of research, while filling in missing details, to provide a comprehensive and reproducible source. Full article

We develop a topological classification of admissible reconstruction operations in generative systems where extended structure is built through repeated local extension subject to compatibility constraints. Reconstruction is formalized as a feasibility-governed process rather than a dynamical or metric one, with admissibility determined by the accumulation of obstruction under composition. Using loop diagnostics, we identify global incompatibilities that are invisible to local extension rules but become unavoidable under closed composition. Under mild and realization-independent assumptions, including indefinite continuation and finite interface capacity, we show that persistent nontrivial obstruction is possible only when it is supported on codimension-2 subsets of the reconstructed domain. This result induces a small number of topological universality classes distinguished by the existence and stability of loop-detectable obstruction. The framework is model-agnostic and applies equally to discrete, combinatorial, and continuum reconstructions, providing a topological explanation for the ubiquity of codimension-2 defects in generative systems. Full article

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

Point-the-bit rotary steerable systems (RSSs) achieve trajectory build-up through the coupled action of internal steering offset, bit attitude change, bottom hole assembly (BHA) flexure, and nonlinear wellbore interaction. Unlike conventional rigid or quasi-static BUR models, this study developed a 3D dynamic finite element model for point-the-bit RSS. The drill string was discretized using Euler–Bernoulli beam elements, with an equivalent “hinge-deflection angle” constraint introduced at the steering unit. Relative angle loading was imposed using the penalty function method, with nonlinear boundary conditions (bit–formation interaction and borehole friction) coupled into the model. Based on the established model, the effects of deflection angle, weight on bit (WOB), and rotary speed were systematically quantified. The results show that when the deflection angle increases from 0.5° to 1.5°, the average BUR rises from 1.452°/30 m to 4.251°/30 m; when the WOB increases from 60 kN to 100 kN, the average BUR increases from 2.281°/30 m to 2.814°/30 m. Within the range of 50–90 r/min, rotary speed has a limited effect on the average BUR, but it can alter the characteristics of transient fluctuations. This approach provides a robust theoretical basis for BUR evaluation, parameter optimization, and control strategy design for rotary steerable tools. Full article

Reservoir computing (RC) is an efficient framework for processing time-series data. This work investigates the synchronization of two independently trained reservoir computers that, after training, operate without external input from the chaotic system and interact solely through symmetric linear coupling. This approach addresses a gap in existing reservoir computing-based synchronization studies, which predominantly rely on master–slave or system-driven configurations. In this work, we first build and train two reservoir computing models based on 3D nonlinear chaotic maps and hyperchaotic systems and then introduce a symmetric linear coupling mechanism between them. This study demonstrates that reservoir computing can accurately reproduce the short-term dynamics of chaotic systems and provides insight into the interactions between learned dynamical models, while also helping us understand how complex systems connect and operate collectively. We use this systematic approach to establish a framework for understanding how two trained reservoir computers interact under varying coupling strengths, enabling a detailed investigation of their synchronization behavior. To demonstrate the adaptability of the proposed framework to diverse dynamical behaviors, we systematically investigated three discrete chaotic and hyperchaotic systems: (1) discrete 3D sinusoidal map with discrete Lorenz attractor, (2) 3D sinusoidal map with conjoined Lorenz twin attractor, and (3) 3D quadratic hyperchaotic map. For performance evaluation, we trained coupled RCs and computed the synchronization error for different coupling strengths. We also present phase portraits and time-series plots of the attractors and RCs, along with the synchronization error as a function of the coupling strength, thereby demonstrating the possibility of synchronization of two linearly coupled RCs, which are independently trained on discrete, three-dimensional chaotic and hyperchaotic systems. Full article

This study investigates the effects of vehicle air-conditioning parameters on cabin thermal environment and occupant comfort. Computational fluid dynamics and discrete particle simulations involving different inlet-vent angles, inlet relative humidity (RH) levels, and occupant counts were conducted to analyze airflow, temperature, and RH. Thermal comfort was assessed using predicted mean vote (PMV), predicted percentage of dissatisfied (PPD), equivalent homogeneous temperature, and mean age of air (MAA). As a result, the uniform airflow at a 30° inlet angle provided the best global thermal comfort based on PMV (0.49) and PPD (10.02), whereas a 0° inlet angle improved local comfort around the chest area. Maintaining an inlet RH of 40–50% enhanced overall thermal comfort. Increasing the occupant counts raised the average cabin temperature to 301.76 K (Case 9), while also affecting local airflow patterns and MAA distributions; the addition of rear-seat occupants increased the local temperature around the driver’s left hand. These findings provide practical guidance for vehicle heating, ventilation, and air-conditioning system design, indicating that ventilation strategies should consider global comfort indices, localized airflow, thermal patterns, and particle removal performance. Overall, this parametric study highlights the association between vehicle cabin conditions and thermal comfort, providing baseline data for digital twin–based adaptive ventilation systems. Full article

Background: The systematic identification of transcriptional repressors remains challenging, as current inference frameworks are predominantly optimized for accessible chromatin, leaving regulatory signals embedded within repressive domains undercharacterized. Methods: Here, we present NuRepress, a computational framework that predicts candidate transcriptional repressors by integrating repressive chromatin architecture, functional signatures, and transcriptional outcomes. NuRepress first identifies well-phased nucleosome arrays within repressive chromatin. These arrays are treated as discrete structural units that capture characteristic local chromatin organization associated with regulatory activity. Since distinct Tn5 cut signal patterns often imply divergent regulatory functions, the framework stratifies these arrays into potential functional subtypes. By synthesizing the quantified repressive efficacy of each subtype with spatial motif enrichment and observed transcriptional dynamics, NuRepress systematically prioritizes and ranks candidate repressors. Results: Our analysis indicated that well-phased nucleosome arrays exhibited accessibility-defined organizational patterns with distinct repressive efficacies, and that these patterns were also observed across species, suggesting that the structural principles captured by NuRepress might extend beyond one specific biological system. Positional motif analysis revealed that distinct TFs exhibited different spatial preferences relative to well-phased nucleosome arrays, suggesting scale-specific preferences for their interactions with these organized chromatin structures. When applied to pancreatic cancer progression, NuRepress identified changes in nucleosome organization associated with stage-specific transcriptional remodeling, highlighting candidate repressors of key oncogenic drivers. Conclusions: NuRepress establishes a structure-aware strategy for repressor inference that extends regulatory genomics beyond accessibility-centered paradigms. By linking well-phased nucleosome organization to transcriptional outcomes, it provides a principled framework for dissecting transcriptional repression across diverse biological settings. Full article

Characterizing multiscale fracture networks in shale gas reservoirs remains challenging, while the limited applicability of conventional continuum-based models and insufficient multi-objective coordination often lead to low efficiency in development optimization. To address these issues, this study proposes a production history matching and multi-objective collaborative optimization framework for shale gas horizontal wells based on an equivalent fractal fracture (EFF) model. By integrating fractal theory with intelligent optimization techniques, a multiscale equivalent fractal permeability tensor is constructed, forming a hybrid machine-learning framework that combines physics-based fractal constraints with data-driven learning for efficient representation of complex fracture networks. Microseismic event clouds were converted into continuous fracture-density and fractal-geometry descriptors through denoising, temporal alignment, and spatial interpolation, and these descriptors were mapped to the equivalent fractal fracture model to dynamically update key flow parameters for history matching and parameter inversion. On this basis, a multi-objective collaborative optimization strategy is developed to achieve simultaneous time-varying fracture characterization and dynamic regulation of development parameters. Comparative results indicate that the EFF-based approach yields a production prediction error of 6.8%, slightly higher than the 4.2% obtained using discrete fracture network (DFN) models, while requiring only one-eighteenth of the computational time. Using the net present value (NPV) as the unified objective function, constraints are imposed on bottom-hole flowing pressure, flowback rate and system switching time for optimization. With the optimized pressure drop being more uniform and the gas saturation distribution being more balanced, it is verified that “EFF + NPV” can achieve the coordinated optimization of “production capacity—decline—cost” and enhance the development efficiency. Full article

In this paper, based on the Caputo-like delta fractional difference operator, we will present a fractional discrete model of a 4D Power System. We present an extension of the popular integer-order single-machine infinite-bus formulation to two fractional cases, one with commensurate (equal) fractional orders and another incommensurate (not equal). This extension captures long-memory effects in dynamics and thus offers a consistent mathematical description of the nonlinear behavior of power systems. The orders of the fractional models are analyzed numerically. Using time series evolution, phase-space plots, bifurcation maps, Lyapunov spectra, and the 0–1 chaos test, spectral entropy and $C_0$ complexity metrics, we identify chaotic regimes. Additionally, techniques for controlling chaos are explored to stabilize and regulate the dynamics of the system. Both the fractional formulations exhibit richer dynamical features than their integer counterparts, and for the incommensurate case, the sensitivity to the fractional variations is larger, generating complex nonlinear oscillations. The fractional discrete power system framework provides a new perspective for studying instability, the voltage collapse phenomenon, and chaotic oscillations in power engineering applications. Full article

To address the bottlenecks of low sample efficiency and poor control accuracy in traditional single-layer reinforcement learning during autonomous driving overtaking, this paper proposes an overtaking decision and control system based on hierarchical reinforcement learning to decouple complex tasks in spatial and temporal dimensions. A heterogeneous two-layer architecture is constructed, where the upper layer adopts the Proximal Policy Optimization algorithm to generate macroscopic discrete decisions, while the lower layer employs Twin Delayed Deep Deterministic Policy Gradient combined with Long Short-Term Memory to achieve smooth continuous control of steering and acceleration by perceiving temporal features of dynamic obstacles. A composite reward mechanism, integrating hard safety constraints and soft efficiency incentives, is designed to balance safety, efficiency, and comfort. Experimental results in complex scenarios with multiple interfering vehicles and random lane-changing behaviors demonstrate that the proposed system improves the training convergence speed by approximately 30% within 500,000 steps compared to single-layer algorithms. In tests across varying traffic densities, the system achieves a 98.3% success rate in medium-density scenarios with a collision rate of only 0.6%. In high-density challenges, the success rate remains above 95%, with the collision rate reduced by about 80% compared to baseline models. Furthermore, the lateral control deviation is strictly limited to within 0.2 m, and the longitudinal safety distance remains stable above 5 m. This system provides a robust, high-efficiency paradigm for autonomous overtaking. Full article

Video-based action recognition for neural rehabilitation—spanning stroke recovery, Parkinsonian gait assessment, and cerebral palsy monitoring—faces critical challenges, including temporal ambiguity, non-causal motion correlations, and the absence of causally grounded dynamics modeling. While transformer-based architectures achieve strong performance, they often exploit spurious temporal and environmental cues, limiting reliability in safety-critical clinical settings. We propose NeuroPrisma, a neuro-prismatic video framework that integrates frequency-domain spectral decomposition with causal intervention under Structural Causal Models (SCMs) via the backdoor criterion. NeuroPrisma introduces (i) a Prismatic Spectral Attention (PSA) module, which applies discrete Fourier transforms to decompose temporal features into multi-scale frequency bands, disentangling slow postural dynamics from rapid corrective movements, and (ii) a Causal Intervention Layer (CIL), which performs do-calculus-based backdoor adjustment to remove confounding influences and produce causally invariant representations. PSA preconditions representations prior to intervention, improving confounder estimation and causal robustness. Extensive evaluation against seven state-of-the-art models (I3D, SlowFast, TimeSformer, ViViT, Video Swin Transformer, UniFormerV2, and VideoMAE) demonstrates that NeuroPrisma achieves 98.7% Top-1 accuracy on UCF101, 82.4% on HMDB51, 71.2% on Something-Something V2, and 91.5%/95.8% on NTU RGB+D (Cross-Subject/Cross-View), consistently outperforming prior methods. It further reduces the Causal Confusion Score (CCS) by 42.3%, indicating substantially lower reliance on spurious correlations, while maintaining real-time performance with 23.4 ms latency per 16-frame clip on an NVIDIA A100 GPU. All improvements are statistically significant (p < 0.001, Cohen’s d = 0.72–1.24). Evaluation was conducted exclusively on benchmark datasets (UCF101, HMDB51, Something-Something V2, and NTU RGB+D) under controlled conditions, without direct clinical validation on neurological patient cohorts. Overfitting was mitigated using three random seeds (42, 123, 456), RandAugment, Mixup (α = 0.8), weight decay (0.05), and early stopping. Cross-dataset generalization from UCF101 to HMDB51 without fine-tuning achieved 76.2% Top-1 accuracy. Future work will focus on prospective clinical validation across stroke, Parkinson’s disease, and cerebral palsy populations, including correlation with standardized clinical assessment scales such as Fugl–Meyer, UPDRS, and GMFCS. These results establish NeuroPrisma as a causally grounded and computationally efficient framework for reliable, real-time movement assessment in clinical rehabilitation systems. Full article

This study examines how the COVID-19 period is associated with changes in digital payment usage, rather than simply whether adoption increased, in Saudi Arabia using monthly data from January 2019 to July 2025. An Interrupted Time Series (ITS) approach is employed to assess both the immediate and long-term effects associated with the pandemic on a digital payment Intensity (DPI) index constructed from national point-of-sale (POS) transaction data to capture aggregate electronic payment usage relative to cash withdrawals. The results show that the onset of the COVID-19 period is associated with a sharp and statistically significant one-time increase of approximately 7 to 13% in digital payment intensity, followed by stabilization at a higher level rather than sustained acceleration. This finding challenges the common view that digital payment adoption followed a continuously accelerating path, instead showing that the pandemic induced a discrete upward shift without altering the underlying growth trajectory. The estimated effects remain robust across multiple model specifications, including dynamic ITS models, seasonal adjustments, alternative break dates, exclusion of overlapping usage variables, and parsimonious infrastructure-only models. Inflation and ATM usage consistently show negative associations with digital payment intensity, highlighting the role of macroeconomic stability and cash substitution in shaping payment behavior. The study therefore offers a more nuanced interpretation of post-pandemic digital adoption by showing that the main effect of COVID-19 was a one-time level shift rather than a lasting change in growth dynamics. Focusing on aggregate usage intensity rather than access or account ownership, it provides a system-level perspective on digital payment behavior in response to large-scale shocks. Overall, the evidence suggests that the pandemic period coincided with a discrete upward realignment in digital payment usage in Saudi Arabia, reflecting the interaction between crisis-driven behavioral change and strong pre-existing digital infrastructure under Vision 2030. Full article

During the rainy season, open-channel irrigation systems (OCISs) in the hilly regions of southern China simultaneously undertake flood discharge and storage tasks, which are critical for flood mitigation, rainwater resource utilization, and long-term water security in climate-vulnerable monsoon regions. However, existing methods typically adopt a decoupled framework that separates optimization calculations from rule corrections, often leading to repeated “optimize–correct–reoptimize” iterations and struggling to coordinate the coupling between channel water level evolution and gate operation rules, resulting in frequent gate movements, intensified water level fluctuations, and elevated operational risks. To address these challenges, this study proposes a hybrid model predictive control method (HyMPC) for flood regulation in irrigation canal systems. The method jointly optimizes discrete gate opening and closing states with continuous water level dynamics within a receding prediction horizon. It employs discrete variables to represent gate states and water level zoning, continuous variables to describe channel water level processes, and an integrator-delay model to establish bidirectional coupling between them, enabling coordinated gate group control under combined flood discharge and storage conditions. Taking the flood event from 17 to 20 July 2020, in the Shi River Irrigation District, Anhui Province, China, as a case study, the proposed method was validated through comparative experiments. Results show that, compared with conventional MPC-based canal control models, the method improves gate regulation smoothness (13.33% reduction in the dimensionless integrated absolute flow change), water level stability (26.08% reduction in the high-frequency component of water level fluctuations), and rainwater resource utilization efficiency (6.98% improvement). Scenario analysis further demonstrates that the method can effectively enhance regulation stability and rainwater resource utilization while ensuring flood safety, providing a robust technical pathway and quantifiable tool for adaptive, integrated flood–drought management in irrigation canal systems. Full article

This paper develops a rigorous inferential framework for a class of Gaussian stochastic processes driven by white noise with constant drift, whose temporal evolution is governed by a Caputo fractional derivative of order ${\displaystyle \alpha \in (1/2,1)}$. The model belongs to the family of fractional Volterra processes, where memory is generated by the dynamics themselves rather than by correlated noise. We derive explicit analytical expressions for the mean, variance, and covariance structure of the solution, thereby characterizing in a precise manner how the fractional order ${\displaystyle \alpha }$ governs both variance growth and the strength of temporal dependence. In particular, the process exhibits correlated increments and a power-law variance scaling of order ${\displaystyle t^{2\alpha -1}}$, highlighting the dual role of ${\displaystyle \alpha }$ as a regularity and memory parameter. Building on this structural analysis, we address the statistical problem of estimating the parameter vector ${\displaystyle (\mu ,\sigma ,\alpha )}$ from discrete-time observations. Two complementary procedures are proposed for the estimation of the fractional order: a variance-growth method based on log–log regression of empirical variances, and a wavelet-based estimator exploiting multi-scale scaling properties of the process. For the drift and diffusion parameters ${\displaystyle (\mu ,\sigma )}$, we construct explicit Gaussian pseudo-maximum likelihood estimators derived from the Volterra covariance structure of the increment process. We establish unbiasedness, ${\displaystyle L^2}$-convergence, strong consistency, and asymptotic normality for all estimators. Furthermore, we derive Berry–Esseen type bounds that quantify the rate of convergence toward the Gaussian law, providing sharp distributional approximations in a genuinely fractional and non-Markovian setting. A Monte Carlo study is carried out, using high-resolution Volterra discretizations, large-scale simulation budgets, covariance-structured linear algebra, and multi-scale diagnostic tools. The numerical experiments confirm the theoretical convergence rates, demonstrate the finite-sample reliability of the estimators, and illustrate the sensitivity of the process dynamics to the fractional order ${\displaystyle \alpha }$: smaller values of ${\displaystyle \alpha }$ produce stronger memory effects and higher variability, while values closer to one lead to smoother and more stable trajectories. The proposed methodology unifies statistical inference for long-memory Gaussian processes with fractional differential stochastic dynamics, offering a coherent analytical and computational framework applicable in areas such as quantitative finance, anomalous diffusion in physics, hydrology, and engineering systems with hereditary effects. Full article

We develop structure-preserving variational integrators for non-autonomous Lagrangian systems by extending the prolongation–collocation variational integrator framework to explicitly time-dependent dynamics. The proposed method is obtained by discretizing Hamilton’s principle for non-autonomous Lagrangians, leading to a family of discrete Lagrangian functions defined at a fixed time step. By combining Hermite interpolation, the Euler–Maclaurin quadrature formula, and collocation applied to the Euler–Lagrange equations and their prolongations, the resulting scheme retains key qualitative properties of variational integrators, including a discrete symplectic (or cosymplectic) structure and favorable long-time behavior. We clarify the relationship between the proposed integrator and classical variational integrators for autonomous systems, showing that the method naturally reduces to the standard prolongation–collocation formulation in the time-independent case. Numerical experiments on representative examples illustrate the effectiveness of the approach and demonstrate its advantages over standard integration methods for non-autonomous systems. Full article