Search Results (81)

In response to the poor soil loosening effect of the previous bionic four-bar deep loosening mechanism, this study optimized the bionic motion trajectory according to agronomic requirements, established a trajectory synthesis optimization model of the Stephenson III six-bar mechanism, and solved it using an improved differential evolution algorithm to design a six-bar deep loosening mechanism achieving the optimized trajectory. Based on the Box–Behnken experiment, a regression model of the mechanism’s process parameters and performance indicators was established, and multiple indicators were integrated into a single objective via a satisfaction function. The optimal process parameters obtained were: entry angle 99.61°, shovel distance 185 mm, forward speed 0.29 $\mathrm{m}/\mathrm{s}$, and input speed $5\pi$ rad/s. A comparative simulation using the Discrete Element Method (DEM) showed that, compared to the bionic four-bar mechanism, the six-bar mechanism reduces resistance by 9.91%, increases soil-breaking capacity by 4.23%, reduces shallow disturbance by 14.43%, increases deep disturbance by 29.54%, and improves overall disturbance effect by 42.71%, verifying the effectiveness of agronomic-driven bionic trajectory optimization. Indoor soil tank experiments measured an average resistance of 258.83 N, with a relative error of 8.67% compared to the simulation result (281.32 N). The experiments and simulations were consistent in soil-breaking layer range, soil layer disturbance range, and soil discharge state, validating the model and the six-bar deep loosening mechanism. Full article

With the increasing penetration of renewable energy generation, analysis of the transient characteristics of doubly fed induction generators, as the mainstream wind turbine configuration, is made highly significant both theoretically and practically. However, manufacturers treat the control parameters as confidential commercial secrets, rendering them a “black box”. Parameter identification is fundamental for studying transient characteristics and system stability. Existing identification methods achieve accurate results only under moderate or severe voltage dip faults. To address this limitation, this paper proposes a control parameter identification method based on the adaptive differential evolution algorithm, suitable for DFIG time-domain simulation models. This method enables accurate parameter identification even during mild voltage dips. Firstly, a trajectory sensitivity analysis is employed to evaluate the difficulty of identifying each parameter, establishing the identification sequence accordingly. Secondly, based on the control loop where each parameter resides, the time-domain expressions are discretized to formulate the fitness function. Finally, the identified control parameters are compared against their true values. The results demonstrate that the proposed identification method achieves high accuracy and robustness while maintaining a rapid identification rate. Full article

The Wind Farm Layout Optimization Problem (WFLOP) aims to improve wind energy utilization and reduce wake-induced power losses through optimal placement of wind turbines. Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely adopted due to their suitability for discrete optimization tasks, yet they suffer from limited global exploration and insufficient convergence depth. Differential evolution (DE), while effective in continuous optimization, lacks adaptability in discrete and nonlinear scenarios such as WFLOP. To address this, the fractional-order differential evolution (FODE) algorithm introduces a memory-based difference mechanism that significantly enhances search diversity and robustness. Building upon FODE, this paper proposes FQFODE, which incorporates reinforcement learning to enable adaptive adjustment of the evolutionary process. Specifically, a Q-learning mechanism is employed to dynamically guide key search behaviors, allowing the algorithm to flexibly balance exploration and exploitation based on problem complexity. Experiments conducted across WFLOP benchmarks involving three turbine quantities and five wind condition settings show that FQFODE outperforms current mainstream GA-, PSO-, and DE-based optimizers in both solution quality and stability. These results demonstrate that embedding reinforcement learning strategies into differential frameworks is an effective approach for solving complex combinatorial optimization problems in renewable energy systems. Full article

Mathematical modeling is indispensable in oncology for unraveling the interplay between tumor growth, vascular remodeling, and therapeutic resistance. We present a hybrid modeling framework (continuum-discrete) and present its hybrid mathematical formulation as a coupled partial differential equation–agent-based (PDE-ABM) system. It couples reaction–diffusion fields for oxygen, drug, and tumor angiogenic factor (TAF) with discrete vessel agents and stochastic phenotype transitions in tumor cells. Stochastic phenotype switching is handled with an exact Gillespie algorithm (a Monte Carlo method that simulates random phenotype flips and their timing), while moment-closure methods (techniques that approximate higher-order statistical moments to obtain a closed, tractable PDE description) are used to derive mean-field PDE limits that connect microscale randomness to macroscopic dynamics. We provide existence/uniqueness results for the coupled PDE-ABM system, perform numerical analysis of discretization schemes, and derive analytically tractable continuum limits. By linking stochastic microdynamics and deterministic macrodynamics, this hybrid mathematical formulation—i.e., the coupled PDE-ABM system—captures bidirectional feedback between hypoxia-driven angiogenesis and resistance evolution and provides a rigorous foundation for predictive, multiscale oncology models. Full article

Modeling risk evolution in financial networks presents both practical and theoretical challenges, particularly during periods of heightened systemic stress. This issue has gained urgency recently in China as it faces unprecedented financial strain, largely driven by structural shifts in the real estate sector and broader economic vulnerabilities. In this study, we combine Fractional-order Partial Differential Equations (FoPDEs) with network-based analysis methods, proposing a hybrid framework for capturing and modeling systemic financial risk, which is quantified using the $\Delta CoVaR$ algorithm. The FoPDEs model is formulated based on reaction–diffusion equations and discretized using the Caputo fractional derivative. Parameter estimation is conducted through a composite optimization strategy, and numerical simulations are carried out to investigate the underlying mechanisms and dynamic behavior encoded in the equations. For empirical evaluation, we utilize data from China’s financial and real estate sectors. The results demonstrate that our model achieves a Mean Relative Accuracy (MRA) of 95.5% for daily-frequency data, outperforming LSTM and XGBoost under the same conditions. For weekly-frequency data, the model attains an MRA of 91.7%, exceeding XGBoost’s performance of 90.25%. Further analysis of parameter dynamics and event studies reveals that the fractional-order parameter $\alpha$, which controls the memory effect of the model, tends to remain low when $\Delta CoVaR$ exhibits sudden surges. This suggests that the model assigns greater importance to past data during periods of financial shocks, capturing the persistence of risk dynamics more effectively. Full article

This article examines the historical evolution, contemporary dynamics, and future trajectory of China’s legal and judicial framework for pangolin protection. By reviewing over seventy years of regulatory changes, case law, and policy implementation, it outlines three distinct phases: the early emphasis on pangolins as medicinal and export resources (1949–1989); the phase of conflicted protection and utilization under regulatory expansion (1989–2020); and the post-2020 shift toward judicial activism and ecological civil litigation. We then highlight the long-standing contradiction between legislative protection and continued medicinal use, particularly the centuries-old use of pangolins and their derivatives in traditional Chinese medicine, a practice still acknowledged within certain state policies and regulatory frameworks, showing how these inconsistencies enabled persistent illegal exploitation despite regulatory controls. Through systematic analysis of public court records and case databases, the policy historical records reveal a marked increase in environmental public interest litigation since 2020. These lawsuits, often attached to criminal prosecutions, signal a transition from merely punitive approaches to restorative ones—anchored in ecological valuation of species and their services. Case studies illustrate how courts now impose not only wildlife resource loss fees, but also punitive damages and compensation for ecological service function loss. The article will elaborate in detail on the distinctions and interrelations among these types of compensation. The landmark Case No.17 also demonstrates this paradigm shift, wherein courts recognized pangolins’ role in balancing forest ecosystems. However, significant challenges persist. Valuation methodologies lack uniform standards; while the ecological value of pangolins has been recognized, their inherent value as individuals has not been emphasized within the legal system; judicial discretion varies across jurisdictions; and public interest organizations remain underutilized in litigation. Moreover, while the crackdown on organized crime succeeded in curbing mass trafficking, smaller-scale violations tied to cultural consumption for medicine use persist. The article concludes that judicial innovations, such as ecological judicial restoration bases and integration into China’s draft Ecological Environment Code, offer promising pathways forward. To enhance efficacy, it calls for standardization in ecological valuation, strengthened civil society participation, and nuanced differentiation in penal strategies between minor and serious offenses. This study ultimately positions judicial reform as the cornerstone of China’s evolving pangolin conservation strategy. Full article

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by developing an explicit quantum algorithm that implements the time evolution of a second-order time-discretized version of the Lorenz model. The Lorenz model is a celebrated system of nonlinear ordinary differential equations that has been extensively studied in the contexts of climate science, fluid dynamics, and chaos theory. Our algorithm possesses a recursive structure and requires only a linear number of copies of the initial state with respect to the number of integration time-steps. This provides a significant improvement over previous approaches, while preserving the characteristic quantum speed-up in terms of the dimensionality of the underlying differential equations system, which similar time-marching quantum algorithms have previously demonstrated. Notably, by classically implementing the proposed algorithm, we showcase that it accurately captures the structural characteristics of the Lorenz system, reproducing both regular attractors–limit cycles–and the chaotic attractor within the chosen parameter regime. Full article

Due to the limited endurance of UAVs, especially in scenarios involving large areas and dense target nodes, it is challenging for multiple UAVs to complete diverse tasks while ensuring timely execution. Toward this, we propose a cross-platform system consisting of an aircraft carrier (AC) and multiple UAVs, which makes unified task planning for included heterogeneous platforms to maximize the efficiency of the entire combat system. The carrier-based UAV swarm mission planning problem is formulated to minimize completion time and resource utilization, taking into account large-scale targets, multi-type tasks, and multi-obstacle environments. Since the problem is complex, we design a decoupled framework to simplify the solution by decomposing it into two levels: upper-level AC path planning and bottom-level multi-UAV cooperative mission planning. At the upper level, a drop point determination method and a discrete genetic algorithm incorporating improved A* (DGAIIA) are proposed to plan the AC’s path in the presence of no-fly zones and radar threats. At the bottom level, an improved differential evolution algorithm with a market mechanism (IDEMM) is proposed to minimize task completion time and maximize UAV utilization. Specifically, a dual-switching search strategy and a neighborhood-first buying-and-selling mechanism are developed to improve the search efficiency of the IDEMM. Simulation results validate the effectiveness of both the DGAIIA and IDEMM. An animation of the simulation results is available at simulation section. Full article

This paper proposes a novel integer-mixed probabilistic optimal power flow (IM-POPF) strategy for frequency regulation in islanded microgrids under uncertain operating conditions. Existing load-shedding approaches face critical limitations: continuous frameworks fail to reflect the discrete nature of actual load disconnections, while deterministic models inadequately capture the stochastic behavior of renewable generation and load variations. The proposed approach formulates load shedding as an integer optimization problem where variables are categorized as integer (load disconnection decisions at specific nodes) and continuous (voltages, power generation, and steady-state frequency), better reflecting practical power system operations. The key innovation combines integer load-shedding optimization with efficient uncertainty propagation through Unscented Transformation, eliminating the computational burden of Monte Carlo simulations while maintaining accuracy. Load and renewable uncertainties are modeled as normally distributed variables, and probabilistic constraints ensure operational limits compliance with predefined confidence levels. The methodology integrates Differential Evolution metaheuristics with Unscented Transformation for uncertainty propagation, requiring only 137 deterministic evaluations compared to 5000 for Monte Carlo methods. Validation on an IEEE 33-bus radial distribution system configured as an islanded microgrid demonstrates significant advantages over conventional approaches. Results show 36.5-fold computational efficiency improvement while achieving 95.28% confidence level compliance for frequency limits, compared to only 50% for deterministic methods. The integer formulation requires minimal additional load shedding (21.265%) compared to continuous approaches (20.682%), while better aligning with the discrete nature of real-world operational decisions. The proposed IM-POPF framework successfully minimizes total load shedding while maintaining frequency stability under uncertain conditions, providing a computationally efficient solution for real-time microgrid operation. Full article

Fluid flow and mass transfer processes in some fractured aquifers are negligible in the low-permeability rock matrix and occur mainly in the fracture network. In this work, we consider coupled variable-density flow (VDF) and mass transport with dissolution in discrete fracture networks (DFNs). These three processes are ruled by nonlinear and strongly coupled partial differential equations (PDEs) due to the (i) density variation induced by concentration and (ii) fracture aperture evolution induced by dissolution. In this study, we develop an efficient model to solve the resulting system of nonlinear PDEs. The new model leverages the method of lines (MOL) to combine the robust finite volume (FV) method for spatial discretization with a high-order method for temporal discretization. A suitable upwind scheme is used on the fracture network to eliminate spurious oscillations in the advection-dominated case. The time step size and the order of the time integration are adapted during simulations to reduce the computational burden while preserving accuracy. The developed VDF-DFN model is validated by simulating saltwater intrusion and dissolution in a coastal fractured aquifer. The results of the VDF-DFN model, in the case of a dense fracture network, show excellent agreement with the Henry semi-analytical solution for saltwater intrusion and dissolution in a coastal aquifer. The VDF-DFN model is then employed to investigate coupled flow, mass transfer and dissolution for an injection/extraction well pair problem. This test problem enables an exploration of how dissolution influences the evolution of the fracture aperture, considering both constant and variable dissolution rates. Full article

Evolution equations with fractional-time derivatives and singular memory kernels are used for modeling phenomena exhibiting hereditary properties, as they effectively incorporate memory effects into their formulation. Time-fractional partial integro-differential equations (FPIDEs) represent a significant class of such evolution equations and are widely used in diverse scientific and engineering fields. In this study, we use the $\mathrm{sinc}$-collocation and iterative Laplace transform methods to solve a specific FPIDE with a weakly singular kernel. Specifically, the $\mathrm{sinc}$-collocation method is applied to discretize the spatial domain, while a combination of numerical techniques is utilized for temporal discretization. Then, we prove the convergence analytically. To compare the two methods, we provide two examples. We notice that both the $\mathrm{sinc}$-collocation and iterative Laplace transform methods provide good approximations. Moreover, we find that the accuracy of the methods is influenced by fractional order $\alpha \in (0,1)$ and the memory-kernel parameter $\beta \in (0,1)$. We observe that the error decreases as $\beta$ increases, where the kernel becomes milder, which extends the single-value study of $\beta =1/2$ in the literature. Full article

Wind data are often cyclostationary due to cyclic variations, non-constant variance resulting from fluctuating weather conditions, and structural breaks due to transient behaviour (due to wind gusts and turbulence), resulting in unreliable wind power supply. In wavelet hybrid forecasting, wind prediction accuracy depends heavily on the decomposition level ($L$) and the wavelet filter technique selected. Hence, we examined the efficacy of wind predictions as a function of $L$and wavelet filters. In the proposed hybrid approach, differential evolution (DE) optimises the decomposition level of various wavelet filters (i.e., least asymmetric (LA), Daubechies (DB), and Morris minimum-bandwidth (MB)) using the maximal overlap discrete wavelet transform (MODWT), allowing for the decomposition of wind data into more statistically sound sub-signals. These sub-signals are used as inputs into the gated recurrent unit (GRU) to accurately capture wind speed. The final predicted values are obtained by reconciling the sub-signal predictions using multiresolution analysis (MRA) to form wavelet-MODWT-GRUs. Using wind data from three Wind Atlas South Africa (WASA) locations, Alexander Bay, Humansdorp, and Jozini, the root mean square error, mean absolute error, coefficient of determination, probability integral transform, pinball loss, and Dawid-Sebastiani showed that the MB-MODWT-GRU at $L=3$ was best across the three locations. Full article

The physical health of college students is an important basis for societal development, which directly impacts the competitiveness of future talents and the overall vitality of the nation. To accurately and timely identify the physical health status of college students, a hybrid model of DE-XGBoost is proposed in this study: a discrete coding strategy is designed to solve the XGBoost hyperparameter optimization problem, and differential evolution (DE) is used to achieve global parameter optimization. Based on 20,452 physical test records of a university in 2022, the empirical comparison shows that the accuracy rate, recall rate, and F1 value of the model are improved by 3.5–7.9% compared with support vector machine (SVM), gradient boosting machine (GBM), and multi-layer perceptron (MLP), showing significant performance advantages. This research provides a novel and efficient framework for physical fitness classification, with potential applications in educational curriculum design. Full article

The northwestern slope of the Dongdao Platform in the Xisha Sea exhibits a complex geomorphological structure. Utilizing high-resolution multibeam bathymetric data and 2D seismic profiles, this study systematically reconstructs the slope morphology and its evolutionary processes. The study area displays a distinct threefold zonation: the upper slope (160–700 m water depth) has a steep gradient of 15°–25°, characterized by deeply incised V-shaped channels and slump deposits, primarily shaped by gravity-driven erosion; the middle slope (700–1200 m water depth) features a gentler gradient of 10°–15°, where channels stabilize, adopting U-shaped cross-sections with the development of lateral accretion deposits; the lower slope (1200–1500 m water depth) exhibits a milder gradient of 5°–10°, dominated by a mixture of fine-grained carbonate sediments and hemipelagic mud–marine sediments originating partly from the open ocean and partly from the nearby continental margin. The slope extends from 160 m to 1500 m water depth, hosting the C1–C4 channel system. Seismic facies analysis reveals mass-transport deposits, channel-fill facies, and facies modified by bottom currents—currents near the seafloor that redistribute sediments laterally—highlighting the interplay between fluid activity and gravity-driven processes. The slope evolution follows a four-stage model: (1) the pockmark formation stage, where overpressured gas migrates vertically through chimneys, inducing localized sediment instability and forming discrete pockmarks; (2) the initial channel development stage, during which gravity flows exploit the pockmark chains as preferential erosional pathways, establishing nascent incised channels; (3) the channel expansion and maturation stage, marked by intensified erosion from high-density debris flows, resulting in a stepped longitudinal profile, while bottom-current reworking enhances lateral sediment differentiation; (4) the stable transport stage, wherein the channels fully integrate with the Sansha Canyon, forming a well-connected “platform-to-canyon” sediment transport system. Full article