To construct the micro collision avoidance intelligent unit, efficient solutions have been pursued by modeling a specific collision-sensitive neuron, the lobula giant movement detector (LGMD), identified in the locust’s visual neural system. However, the existing LGMD models cooperating with a threshold-based collision avoidance strategy are less robust in the presence of visual variability, including fluctuations in contrast and noise. Intriguingly, biological research underscores the potential of the single-peak modeling through the η function, suggesting a non-threshold collision avoidance strategy by identifying the peak response moment. Adapting the biological finding to the engineering applications, we establish a novel modeling method based on η-form perceptron, making the response curve behave like the η function. The single peak within the η-function-form response occurs before the collision, offering reliable collision warning cues. Numerical experiments substantiate the effectiveness of the proposed model against visual variability. This study extends the limits of the applications of LGMD models in diverse scenarios, and explores the potential of modeling neural computation in constructing micro-intelligent units.
Efficient delivery of humanitarian supplies is crucial for disaster loss reduction. The Truck-Drone Collaborative Routing Problem (TDRP) has mostly been studied under static demand and ideal road networks, neglecting the dynamic revelation of demand points and nonlinear road capacity degradation from disaster damage. This paper introduces the TDRP with Road Damage and Dynamic Demand (TDRP-RD-DD) to minimize global delivery completion time. We build a full-chain model linking disaster intensity, road capacity, and truck travel speed, and develop an event-driven two-layer rolling optimization framework with locally optimal selection criteria for supply and replenishment modes. To solve this NP-hard problem, we design a Rolling Replenishment-aware Adaptive Large Neighborhood Search (RR-ALNS) with three specialized destroy-repair operators, a capacity- and mode-aware Warm-start strategy, and a replenishment-prioritized repair mechanism for infeasible solutions. Experiments on Solomon instances and a real-world disaster-relief case in Hubei Province show that RR-ALNS closely matches CPLEX on CPLEX-proven small-scale instances, with a 0.47% average gap for the default simulated-annealing setting, while scaling effectively to larger dynamic settings. Compared with three baseline algorithms, RR-ALNS achieves stronger feasibility and overall lower makespan in both benchmark and real-world tests. Ablation and sensitivity analyses further support the value of truck-drone collaboration, collaborative replenishment, and explicit road-damage-to-speed modeling. This work extends dynamic truck-drone routing to post-disaster settings with road damage and demand revelation, and provides quantitative support for emergency logistics decision-making.
To reveal the autonomous emergence mechanism of cooperative behavior in complex systems, this study integrates reinforcement learning with evolutionary game theory to construct a Q-learning-based multi-agent system for the spatial Prisoner’s Dilemma. The concept of spatiotemporal cell is proposed as a microscopic analysis unit to systematically explore the laws governing the emergence, growth, and extinction of cooperation, while quantifying the regulatory effects of parameters through an α -γ phase diagram. Our results show that the emergence of cooperation in the spatial Prisoner’s Dilemma system does not require external interventions as in traditional models. Instead, it relies on the autonomous exploration-belief update-strategy coordination process of agents. Specifically, only when agents within a spatiotemporal cell undergo long-term interactive learning with their neighbors and are triggered by synchronous cooperative-state pulses will they transition from the ground-state belief mode to the excited states. Macroscopically, cooperative behavior exhibits a stable two-periodic oscillation mode. The oscillation amplitude increases with the learning rate α and decreases with the discount factor γ , and a high γ (valuing future rewards) conversely inhibits cooperation. We develop a spatiotemporal cell theoretical framework. Based on this theory, four phases of the system’s collective decision-making behaviors are identified in the α -γ phase diagram: Ground state phase, Cooperative phase, Cooperation growth phase, and Cooperation extinction phase, which are highly consistent with the results of simulation experiments. This study provides a new paradigm and theoretical support for understanding the behavior emergence in ecological and social systems, as well as for the design of engineering systems such as unmanned swarms.
Nanoclay/polymer nanocomposites are increasingly used in lightweight and vibration-sensitive components, where the dynamic response can be strongly governed by nanoscale effects and interphase-mediated load-transfer mechanism. In this study, a unified modeling framework is developed by combining a size-dependent continuum formulation with an explicit interphase-network representation for nanoclay-reinforced polymer nanocomposites. In contrast to conventional homogenization-based approaches, the proposed model incorporates (i) a material length-scale parameter to capture size dependence, (ii) a critical interfacial shear modulus controlling interphase load transfer, and (iii) the intermediate population of intercalated layers to quantify morphology-dependent reinforcement. Model predictions are validated against available experimental measurements, showing consistent agreement across representative systems. Building on the validated framework, dynamic analyses are performed to investigate resonance characteristics and responses under moving-load excitation. The results highlight how interphase quality and size effects can markedly alter dynamic performance, providing an application-oriented tool for parameter identification and design optimization of nanoclay/polymer nanocomposites.
The work devises a novel velocity-prediction lightweight diffusion model (VPLDM) for three-dimensional aircraft aerodynamic inverse design under high-dimensional variable constraints. The model uses a lightweight diffusion modeling paradigm based on a multilayer perceptron, and reconstructs the noise predictions of traditional diffusion models into velocity predictions, which improves the design efficiency and design accuracy. Trained on a dataset of three-dimensional aircraft, the model is able to generate new samples from random vectors that meet the constraints of specific aerodynamic performance indicators. VPLDM achieves higher design accuracy while demonstrating approximately 4 times higher sampling efficiency compared to Denoising Diffusion Probabilistic Model. The model can also generate aircraft schemes with significantly different geometries in the design space, which confirms the typical non-uniqueness of solutions to the aerodynamic inverse design problem for aircraft. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the role of VPLDM in the three-dimensional aircraft aerodynamic inverse design.
