Searcharxiv⌕ Search

arXiv subjects

Suli Liu

Publications and source records attributed to Suli Liu.

4 recordsLinked to original sources
arXiv2026

A data-driven stage-structured host-parasitoid model for optimizing Trichogramma interventions against soybean pod borer (Leguminivora glycinivorella) outbreaks

The soybean pod borer (Leguminivora glycinivorella) poses a severe threat to global soybean production.In this study, we developed a stage-structured host-parasitoid dynamic model that explicitly couples the holometabolous life cycle of the pest with the obligate egg-parasitism mechanism of Trichogramma wasps. Utilizing field monitoring data from Changchun, Jilin Province, key biological parameters were rigorously estimated via the Markov Chain Monte Carlo (MCMC) method.This calibration facilitated the establishment of a precise Economic Injury Level ($Q_{EIL}$) of 0.0389 individuals/$m^2$, based solely on the destructive larval stage. Through theoretical and numerical analyses of different intervention scenarios, we identified an optimal continuous release rate ($C^* = 2.645$) that efficiently suppresses the outbreak without causing wasteful parasitoid accumulation. Furthermore, simulations demonstrate that a 5-day impulsive release interval provides the optimal balance between strict pest suppression and field operational costs. This study bridges the gap between theoretical population dynamics and applied agricultural management, providing a directly applicable mathematical decision-making tool for the precise biological control of crop pests.

q-bio.PE↗
arXiv2025

Multicycle dynamics and high-codimension bifurcations in SIRS epidemic models with cubic psychological saturated incidence

This study investigates bifurcation dynamics in an SIRS epidemic model with cubic saturated incidence, extending the quadratic saturation framework established by Lu, Huang, Ruan, and Yu (Journal of Differential Equations, 267, 2019). We rigorously prove the existence of codimension-three Bogdanov-Takens bifurcations and degenerate Hopf bifurcations, demonstrating the coexistence of three limit cycles within a single epidemiological model, a phenomenon that is rarely documented and exhibits significant dynamical complexity. Our analysis reveals that both the infection rate $\kappa$ (through specific inequality conditions) and psychological effect thresholds critically govern disease dynamics: from complete eradication to various persistence patterns, including multiple periodic oscillations and coexistent steady states. By innovatively applying singularity theory, we characterize the topology of the bifurcation set through the local unfolding of singularities and the identification of nondegenerate singularities for fronts. Numerical simulations verify the emergence of three limit cycles in monotonic parameter regimes and two limit cycles in nonmonotonic regimes. This work advances existing bifurcation research by incorporating higher-order interactions and comprehensive singularity analysis, thereby providing a mathematical foundation for decoding complex transmission mechanisms critical to the design of public health strategies.

math.DS↗
arXiv2025

Modeling the Temperature-Humidity Coupling Dynamics of Soybean Pod Borer Population and Assessing the Predictive Performance of the PCM-NN Algorithm

Against the backdrop of global climate change and agricultural globalization, soybean production is increasingly threatened by pest outbreaks, with Leguminivora glycinivorella (commonly known as the soybean pod borer) being a major pest species. This pest is widely distributed, particularly in northeastern China, the country's primary soybean-producing region, where its outbreaks have significantly affected both yield and quality. Although statistical and mechanistic models have been applied to pest forecasting, existing approaches often fail to effectively integrate climatic factors with pest dynamics and lack sufficient expressive power. To address these limitations, this study proposes a novel pest prediction method based on Physics-Informed Neural Networks (PINNs). Specifically, we formulate a logistic-type ordinary differential equation (ODE) that incorporates microclimate factors, temperature, humidity, and time, to describe the temporal dynamics of the soybean pod borer population. This ODE model is embedded into the PINN framework to develop the Pest Correlation Model Neural Network (PCM-NN), which is used to jointly infer the microclimate-driven parameter function alpha(T, H, t) and fit the pest population dynamics. We evaluate PCM-NN using daily monitoring data of soybean pod borer collected in Changchun, Jilin Province, from July to September during 2020-2023. Experimental results demonstrate that PCM-NN preserves biological interpretability while exhibiting strong nonlinear representational capacity, offering a feasible pathway for pest modeling and forecasting under multi-factor climatic conditions. This approach provides valuable support for agricultural pest monitoring, prevention, and control strategies.

math.DS↗