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Pine wilt disease (PWD) is mainly spread by Monochamus alternatus (in short, M. alternatus). Woodpecker, as the natural predator of M. alternatus, is considered for biological prevention and controlling the PWD. In this paper, we propose a new M. alternatus-woodpecker model with nonlocal competition and memory-based diffusion, which makes the model more realistic for the PWD control. We focus on the dynamics and bifurcations of the model with various combinations of the memory diffusion and nonlocal competition. It is shown that the nonlocal competition can only cause the stable constant steady state to lose stability, while the memory-based diffusion can induce unstable spatially inhomogeneous periodic solutions due to Hopf bifurcation. Consequently, we can explain the spatiotemporal heterogeneity problem in ecology by innovatively using mathematical modelling. Normal form theory with the multiple time scales method is applied to particularly consider Hopf bifurcation, showing complex dynamical behaviours involving various oscillating motions. Finally, numerical simulations are presented with the parameter values chosen from the real forest data of Yuan'an County, Hubei Province, China, confirming the theoretical results of the spatiotemporal heterogeneity of forest diseases and pests, as well as the PWD control.
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http://dx.doi.org/10.1016/j.mbs.2025.109524 | DOI Listing |
Math Biosci
September 2025
Department of Mathematics, Western University, London, Ontario, N6A 5B7, Canada. Electronic address:
Pine wilt disease (PWD) is mainly spread by Monochamus alternatus (in short, M. alternatus). Woodpecker, as the natural predator of M.
View Article and Find Full Text PDFSci Rep
August 2025
School of Computer Science and Information Engineering, Harbin University of Commerce, Harbin, 150028, China.
The message-passing paradigm on graphs has significant advantages in modeling local structures, but still faces challenges in capturing global information and complex relationships. Although the Transformer architecture has become a mainstream approach for nonlocal modeling in many domains, Transformer-based architectures fail to demonstrate competitiveness in popular node-level prediction tasks when compared to mainstream graph neural network (GNN) variants. This can be attributed to the fact that existing research has largely focused on more efficient strategies to approximate the Vanilla Transformer, thereby overlooking its potential in node embedding representation learning.
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Centre for Eudaimonia and Human Flourishing, Linacre College, Oxford University, UK; Centre for Psychedelic Research, Division of Brain Sciences, Imperial College London, UK; Institute of Philosophy, The School of Advanced Study, University of London, UK; Fitzwilliam College, University of Cambridge
Can active inference model consciousness? We offer three conditions implying that it can. The first condition is the simulation of a world model, which determines what can be known or acted upon; namely an epistemic field. The second is inferential competition to enter the world model.
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June 2025
School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
Magnetic resonance images (MRI) denoising aims to obtain clean image for further treatment by doctors. Recently, low-rank tensor methods have achieved amazing results in MRI denoising. Nevertheless, imbalanced matricization from Tucker decomposition and nuclear norm penalty mechanism are incapable of fully characterizing the internal structure information of 3D MR image.
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May 2025
Tianjin University of Science and Technology, Tianjin, 300222, China.
With the advancement of deep learning, robotic grasping has seen widespread application in fields, becoming a critical component in enhancing automation. Accurate and efficient grasping capabilities not only significantly boost productivity but also ensure safety and reliability in complex and dynamic environments. However, current approaches, particularly those based on convolutional neural networks (CNNs), often neglect the hierarchical information inherent in the data and lead to challenges in complex environments with abundant background information.
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