Search Results (1,032)

Accurate flood simulation and forecasting in plain–hilly transition zones remain challenging due to limitations of medium- and low-resolution digital elevation models (DEMs), which often produce discontinuous drainage networks and misaligned confluence paths. This study evaluates an integrated improvement framework that combines DEM stream-burning and automatic parameter calibration to enhance the flood-simulation performance of a physically based distributed hydrological model (the Liuxihe Model). The framework was tested in the Beimiaoji Watershed (upper Huaihe River Basin) using 12 observed flood events: one event for parameter calibration via Particle Swarm Optimization (PSO) and 11 events for independent validation. Model performance was assessed using multiple metrics, including the Nash–Sutcliffe Efficiency (NSE), peak error (PE), and peak-timing error (PT). Results indicate that stream-burning substantially improves river-network extraction, and that the combined application of DEM correction and PSO-based calibration markedly enhances model performance. The findings suggest that the proposed, cost-effective correction–calibration pathway can improve operational flood simulations in terrain-sensitive regions without relying on costly high-resolution DEMs, and thus provides a practical reference for similar basins. Full article

This study constructs a hydraulic-coupled phase-field fracture model based on the phase-field method, employing a granular random distribution model combined with a fractability evaluation index to comprehensively analyze the influence of multiple factors, including the brittleness index, stress difference, and natural fractures, on fracture propagation. The results indicate that fractures in Type I reservoirs with a high proportion of brittle components are more likely to initiate and exhibit extensive damage zones, with fracture propagation following a pattern of avoiding hard regions and favoring soft regions. The horizontal stress difference shows a significant negative correlation with the initiation pressure. Under conditions of small stress differences, mineral heterogeneity dominates the fracture morphology, while under large stress differences, stress orientation plays a predominant role. Additionally, the presence of natural fractures alters the stress field distribution and flow paths, highlighting the importance of accurately predicting the distribution and angular state of natural fractures for forecasting fracture propagation patterns. Finally, a comprehensive fractability evaluation index is established, and reservoir conditions and in situ stress parameters are categorized into three reservoir types for simulation. This study systematically elucidates the multi-factor synergistic mechanism of “brittleness-dominated initiation, stress difference-guided propagation, and natural fracture-disturbed paths.” The findings provide a novel and robust theoretical foundation for optimizing hydraulic fracturing designs and offer significant guidance for the efficient development of unconventional oil and gas resources. Full article

In infrared radiation calculations, the k-distribution method effectively improves the computational efficiency of solving the radiative transfer equation for uniform paths and achieves accuracy comparable to the line-by-line method. However, when applied to highly non-uniform scenarios involving multiple thermodynamic states, such as the infrared radiation from aero-engine nozzles, the computational error increases significantly. This paper proposes a spectral mapping method for multiple thermodynamic states, which iteratively partitions the spectral intervals of the target gas into multiple comonotonic sub-intervals using particle swarm and clustering algorithms. This approach eliminates the blurring effect of traditional k-distribution methods in strongly non-uniform scenarios and enhances the computational accuracy. The study examines the impact of sub-interval partitioning strategies on the accuracy of the gas radiation model, explores the mechanism behind constructing comonotonicity within sub-intervals, and reveals how variations in the comonotonic vector and spectral point clustering strengthen sub-interval comonotonicity. The proposed spectral mapping method and optimization techniques are applied to gas radiation models in typical infrared bands, and the performance of the model is evaluated using results from representative one-dimensional test cases. The results demonstrate that the optimized spectral mapping method reduces the overall relative error of the gas radiation model from 63% to 7.3%, achieving a maximum improvement in computational accuracy of 88.5%. Full article

The Capacitated Vehicle Routing Problem (CVRP) has a wide range of applications in logistics and transportation. Current metaheuristics typically rely on manually added constraints. A hyper-heuristic framework can reduce the dependency on domain-specific knowledge. Therefore, this research proposes a Clustered Simulated Annealing algorithm (CSA). When generating the initial solution of the distribution path, the CSA adopts the Clustered Clarke–Wright Savings algorithm (CCW), the core of which is to use the K-means algorithm to cluster according to the Euclidean distances between the distribution points. The CCW can reduce the search range of the optimization problem by clustering and generating the initial solution quickly, enabling the CSA to perform better in data processing and real-time updates. The CSA then optimizes the initial solution using the Improved Simulated Annealing Hyper-Heuristic algorithm (ISAHH), divided into upper and lower layers. The Improved Simulated Annealing High-Level Heuristic strategy (ISAHLH) is used to select the Low-Level Heuristic operators (LLHs). At the same time, LLHs are used to generate new distribution paths. This research designs an Improved Tabu Low-Level Heuristic operator (ITabuLLH), which can search for several different paths simultaneously in a single iteration, thus improving the convergence speed of the algorithm. ISAHLH and ITabuLLH both use the Unequal Probability Selection mode (UEPS) to speed up the search process. The CSA is tested on the Uchoa benchmark set, and the results verify that the optimal value improvement of the CSA solution is higher than 20% when compared to eleven other algorithms. Full article

Multi-channel common-aperture optical systems, which excel at simultaneous multi-spectral information acquisition, are widely used for image fusion. However, complex systems for long-distance multi-band detection suffer from difficulties in assembly and adjustment and light vignetting. To resolve this, the paper proposes a modular design method that splits the optical path into independent modules: the common-aperture optical path adopts an off-axis reflective beam-shrinking structure to extend the focal length and ensure 100% light input, compared with coaxial multi-channel common-aperture systems. The relay optical path of each spectral channel uses a continuous zoom design for smooth detection–recognition switching. Based on the method, a three-channel common-aperture system is developed integrating visible light (VIS), short-wave infrared (SWIR), and mid-wave infrared (MWIR). The modulation transfer function (MTF) and wavefront distribution of the common-aperture optical path approach the diffraction limit. After integration with the relay optical paths, the system, without global optimization, can achieve the following performance: the root mean square (RMS) across the full field of view (FOV) at different focal lengths for each channel is smaller than the detector pixel size (3.45 μm for VIS, 15 μm for SWIR/MWIR); the MTF exceeds 0.2 at the cutoff frequency. Subsequently, the results of the tolerance analysis verify the feasibility of the design for each module and the advantage of the modular layout in the assembly and adjustment of the system. Finally, the paper discusses the influence of parallel plates on the wavefront distortion of the system and proposes optimization thinking using freeform surfaces. The design results of the study validate the feasibility of the modular layout in simplifying the design and assembly of multi-channel common-aperture optical systems. Full article

Feedrate planning is a critical process in computer numerical control (CNC) machining, playing a key role in ensuring machining quality and improving efficiency. This paper proposes a feedrate planning method based on feedrate profile shaping to satisfy machine axis constraints, including axis velocity, acceleration, and jerk limits. First, the five-axis machining path is represented using parametric curves. By combining the geometric characteristics of the path with machine axis velocity constraints, the upper bound of the feedrate under static constraints is derived. On this basis, machine axis acceleration and jerk constraints are further incorporated to establish feedrate planning criteria, thereby obtaining a distribution of feasible points that satisfies dynamic constraints. Then, a feedrate curve is generated using a profile shaping strategy based on the feasible point distribution, and further optimized through a corner shaping method. As a result, the planned feedrate strictly satisfies machine axis constraints along the entire tool path while ensuring continuity and smoothness of the feedrate profile. Finally, the effectiveness and reliability of the proposed method are validated through simulations of the parametric curve and experimental machining of an impeller blade. Full article

Bio-inspired flow-sensing and actuation mechanisms offer a promising path for enhancing the stability of flapping-wing flying robots (FWFRs) operating in dynamic and noisy environments. This study introduces a bio-inspired sensing and actuation feather unit (SAFU) that mimics the covert feathers of falcons and serves simultaneously as a distributed flow sensor and an adaptive actuation element. Each electromechanical feather (EF) passively detects airflow disturbances through deflection and actively modulates its flaps through an embedded actuator, enabling real-time aerodynamic adaptation. A reduced-order bond-graph model capturing the coupled aero-electromechanical dynamics of the FWFR wing and SAFU is developed to provide a physics-based training environment for a proximal policy optimization (PPO) based reinforcement learning controller. Through closed-loop interaction with this environment, the PPO policy autonomously learns control actions that regulate feather displacement, reduce airflow-induced loads, and improve dynamic stability without predefined control laws. Simulation results show that the PPO-driven SAFU achieves fast, well-damped responses with rise times below 0.5 s, settling times under 1.4 s, near-zero steady-state error across varying gust conditions and up to 50% alleviation of airflow-induced disturbance effects. Overall, this work highlights the potential of bio-inspired sensing-actuation architectures, combined with reinforcement learning, to serve as a promising solution for future flapping-wing drone designs, enabling enhanced resilience, autonomous flow adaptation, and intelligent aerodynamic control during operations in gusts. Full article

With the rapid growth of e-commerce, intra-city express delivery has expanded rapidly, leading to various social issues, such as traffic congestion and air pollution. To address these problems, we focus on designing a multimodal intra-city express system in which parcels are collected from clients via local tours operated by a fleet of identical trucks, temporarily stored in satellite hubs, and then sent to the center hub via underground railway for further sorting and distribution. The problem involves capacitated hub location, client-to-hub allocation, and vehicle routing. Several practical constraints are considered in the routing aspect, including vehicle capacity, time windows, and maximum path length. With these practical considerations, we first formulate a star hub location-routing problem with time windows (SHLRPTW). Second, we use a branch-and-price-and-Benders-cut (BPBC) algorithm to solve it, which combines the Benders decomposition framework and branch-and-price-and-cut (BPC) framework. The BPBC algorithm is tailored, and several acceleration techniques are applied. Third, numerical experiments show that the proposed BPBC algorithm solves more instances and achieves smaller optimality gaps (0.75%) than CPLEX (19.55%) and the pure BPC algorithm (0.83%). The computational times are also critically reduced, with average speed-ups of 74.01 and 5.97, respectively. Furthermore, sensitivity analysis indicates that the BPBC algorithm performs much better than the BPC algorithm when the unit backbone transportation cost is high. Finally, case studies show the usefulness of the proposed model and algorithm. Full article

Severe floor heave in gate roadways under high-intensity longwall mining is primarily controlled by mining-induced stress redistribution. Abutment pressure is preferentially transferred through the coal pillar into the floor, accelerating floor instability. From the perspective of symmetry, mining disturbance breaks the original mechanical symmetry of the coal pillar–roadway system, resulting in asymmetric stress concentration and uneven floor heave. In this study, field monitoring and FLAC3D simulations were conducted for the 12 Upper 301 panel in the Buertai Coal Mine. The objectives were to quantify the sensitivity of coal-pillar loading and floor-heave response under stress redistribution, and to derive implications for pressure-relief design. Field monitoring indicates strong disturbance and large deformation: the maximum roof–floor and rib-to-rib convergences reached 1095 mm and 452 mm, respectively, accompanied by continuous growth of coal-pillar stress during mining. Numerical results show that increasing coal-pillar width enhances stress-bearing capacity and promotes a more symmetric stress distribution, thereby suppressing floor heave. In contrast, increasing the mining advance rate aggravates stress-field asymmetry and intensifies floor uplift. Greater burial depth further strengthens stress concentration and amplifies asymmetric deformation. Based on these findings, a roof-cutting pressure-relief scheme was optimized. This scheme aims to relieve and re-route the asymmetrically transmitted pillar loading. The optimal design adopts a roof-cutting length of 75 m and an angle of 30°, which reconstructs a more symmetric stress-transfer path; reduces the peak side abutment pressure to 8.72 MPa; and limits floor heave to 134.4 mm (control rate: 88.4%). Field application confirms the effectiveness of the proposed symmetry-based pressure-relief design. Full article

This study is grounded in the macro-context of smart agriculture and global food security. Due to population growth and climate change, precise and efficient monitoring of crop distribution and growth is vital for stable production and optimal resource use. Remote sensing combined with deep learning enables multi-scale agricultural monitoring from field identification to disease diagnosis. However, current models face three deployment bottlenecks: high complexity hinders operation on edge devices; scarce labeled data causes overfitting in small-sample cases; and there is insufficient generalization across regions, crops, and imaging conditions. These issues limit the large-scale adoption of intelligent agricultural technologies. To tackle them, this paper proposes a lightweight crop recognition model, MAF-RecNet. It aims to achieve high accuracy, efficiency, and strong generalization with limited data through structural optimization and attention mechanism fusion, offering a viable path for deployable intelligent monitoring systems. Built on a U-Net with a pre-trained ResNet18 backbone, MAF-RecNet integrates multiple attention mechanisms (Coordinate, External, Pyramid Split, and Efficient Channel Attention) into a hybrid attention module, improving multi-scale feature discrimination. On the Southern Hebei Farmland dataset, it achieves 87.57% mIoU and 95.42% mAP, outperforming models like SegNeXt and FastSAM, while maintaining a balance of efficiency (15.25 M parameters, 21.81 GFLOPs). The model also shows strong cross-task generalization, with mIoU scores of 80.56% (Wheat Health Status Dataset in Southern Hebei), 90.20% (Global Wheat Health Dataset), and 84.07% (Corn Health Status Dataset). Ablation studies confirm the contribution of the attention-enhanced skip connections and decoder. This study not only provides an efficient and lightweight solution for few-shot agricultural image recognition but also offers valuable insights into the design of generalizable models for complex farmland environments. It contributes to promoting the scalable and practical application of artificial intelligence technologies in precision agriculture. Full article

With the increasing application of unmanned aerial vehicle (UAV) in multiple fields, the path planning problem has become a key challenge in the optimization domain. This paper proposes an Improved Artificial Lemming Algorithm (IALA), which incorporates three strategies: the optimal information retention strategy based on individual historical memory, the hybrid search strategy based on differential evolution operators, and the local refined search strategy based on directed neighborhood perturbation. These strategies are designed to enhance the algorithm’s global exploration and local exploitation capabilities in tackling complex optimization problems. Subsequently, comparative experiments are conducted on the CEC2017 benchmark suite across three dimensions (30D, 50D, and 100D) against eight state-of-the-art algorithms proposed in recent years, including SBOA and DBO. The results demonstrate that IALA achieves superior performance across multiple metrics, ranking first in both the Wilcoxon rank-sum test and the Friedman ranking test. Analyses of convergence curves and data distributions further verify its excellent optimization performance and robustness. Finally, IALA and the comparative algorithms are applied to eight 3D UAV path planning scenarios and two amphibious UAV path planning models. In the independent repeated experiments across the eight scenarios, IALA attains the optimal performance 13 times in terms of the two metrics, Mean and Std. It also ranks first in the Monte Carlo experiments for the two amphibious UAV path planning models. Full article

This study presents a microfluidic platform for non-Newtonian fluid viscosity sensing, integrating a high-flow-rate flow field stabilizer to mitigate flow uniformity limitations under elevated flow rate conditions. Building upon an established dual-phase laminar flow principle that determines relative viscosity via channel occupancy, this research aimed to extend the measurable viscosity range from 1–10 cP to 1–50 cP, which covers viscosity regimes relevant to biomedical fluids, dairy products during gelation, and low-to-moderate viscosity industrial liquids. A flow stabilizer was developed through computational fluid dynamics simulations, optimizing three key design parameters: blocker position, porosity, and the number of outlet paths. The N5 design proved most effective, providing over 50% reduction in standard deviation for asymmetric velocity distribution in high-flow simulations. The system was validated using simulated blood and dairy samples, achieving over 95% viscosity accuracy with less than 5% sample volume error compared to conventional viscometers. The chip successfully captured viscosity transitions during milk acidification and gelation, demonstrating excellent agreement with standard measurements. This low-volume, high-precision platform offers promising potential for applications in food engineering, biomedical diagnostics, and industrial fluid monitoring, enhancing microfluidic rheometry capabilities. Full article

Model predictive control (MPC) has become one of the most promising control strategies in the field of electric vehicle energy management due to its rolling optimization and explicit constraint processing capabilities. This study analyzes the modeling mechanism and implementation path of MPC in power allocation, regenerative braking and energy collaborative control, which elaborates on the improvement principle of energy efficiency and system stability through predictive modeling and dynamic optimization. The evolution of MPC application in hybrid power systems, vehicle dynamic stability control, and hierarchical optimization control is discussed. The synergistic effect of multi-objective optimization and health-conscious control in energy efficiency improvement and service life extension is analyzed. With the development of artificial intelligence technology, MPC is expanding from model-based deterministic control to the directions of intelligent learning and distributed adaptation. Model uncertainty, computational complexity, and real-time solving efficiency are the main challenges faced by MPC. Future research will focus on the deep integration of model simplification, rapid solving, and intelligent learning to achieve a more efficient and reliable intelligent energy management system. Full article

The regional green patent cooperation network describes the structural characteristics of regional collaborative innovation, and the link prediction of the network can anticipate the overall evolution trend, as well as help organizations identify potential partners for technology collaboration. This paper proposes a link prediction model based on multidimensional features, which integrates prediction indicators of node features, path features, and content features. In the model, the entropy weight method is employed to integrate various node similarity indicators, the heterogeneous influence of intermediate links and nodes is incorporated to fully emphasize the issue of heterogeneous paths, and the content similarity feature indicator based on patent text topic analysis integrates multiple distance similarity metrics. To improve prediction accuracy, the Grey Wolf Optimizer (GWO) method is adopted to determine the optimal weights for the three-dimensional indicators. The comparative experimental results show that the multidimensional prediction model can improve prediction accuracy significantly. Finally, the proposed prediction model is applied to forecast the green patent cooperation network in the Beijing-Tianjin-Hebei region of China, and the prediction results are discussed based on the distribution of agent types and regional distribution. Full article

Achieving safe and stable navigation for autonomous ground vehicles (AGVs) in complex environments remains a key challenge in intelligent robotics. Conventional Model Predictive Path Integral (MPPI) control relies on pseudo-random Gaussian sampling, which often results in non-uniform sample distributions and jitter-prone control sequences, thereby limiting both convergence efficiency and control stability. This paper proposes a trajectory optimization method: Halton-MPPI, which improves MPPI by employing low-discrepancy sampling and modeling temporally correlated perturbations. Specifically, it utilizes the Halton sequence as the sampling basis for control disturbances to enhance spatial coverage, while the Ornstein–Uhlenbeck (OU) process is introduced to impose temporal correlation on control perturbations. This time-consistent noise propagation allows perturbation effects to accumulate over time, thereby expanding trajectory coverage. Large-scale simulations on the BARN dataset demonstrate that the method significantly enhances both trajectory smoothness (MSCX) and control smoothness (MSCU) while maintaining high success rates. Moreover, field tests in outdoor environments validate the effectiveness and robustness of Halton-MPPI, underscoring its practical value for autonomous navigation in complex environments. Full article