Addressing the challenges inherent in complex intelligent planning—specifically high-dimensional discrete decision spaces, strong constraint coupling, and the propensity of traditional single-layer Ising/QUBO modeling to induce variable inflation and penalty imbalance—this paper proposes a hierarchical hybrid Ising simulation framework. This framework is investigated through its application to the problem of multi-agent task assignment and collaborative path planning. The proposed method models task assignment and visit sequencing as an outer-layer QUBO/Ising optimization problem, incorporating techniques such as continuous relaxation, momentum enhancement, noise perturbation, and feedback correction to bolster search capabilities. Concurrently, the inner layer handles path construction, conflict detection, and local repair, feeding the repair information back to the outer layer to establish a closed-loop optimization process. Experimental results demonstrate that this method outperforms comparative approaches across key metrics, including total cost, feasible solution rate, conflict control, and convergence stability.

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In UAV-assisted Mobile Crowd Sensing, ground workers are responsible for basic data collection, while UAVs can perform supplementary sensing in areas that are difficult to cover in a timely manner. To address the limited efficiency of distributed multi-UAV cooperative supplementary sensing in the absence of a central controller, this paper proposes an information-exchange-based distributed multi-UAV cooperative sensing algorithm, termed IE-DDQN. The proposed method formulates the problem as a decentralized partially observable Markov decision process, enables inter-UAV information exchange through short-range Device-to-Device (D2D) links, and employs a neighbor information pooling module to aggregate multi-source high-dimensional messages into low-dimensional representations, which are then combined with local observations for decision making. Experimental results demonstrate that the proposed method outperforms pure-worker and pure-UAV schemes in terms of weighted data value per unit composite cost, task completion rate, and task completion time.

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To address the industry challenges of unclear flow control mechanisms in magnetically driven four-chamber peristaltic pumps and the lack of a quantitative correlation between magnetic membrane deformation and pumping flow rate, a fully numerical simulation method based on multi-physics coupling of magnetic fields, structure, and flow fields was employed to construct a detailed simulation model of a four-chamber symmetric peristaltic pump. Using the maximum deformation intensity of the magnetic membrane as the sole independent variable, we conducted comparative simulations under gradient deformation conditions to accurately calculate the corresponding steady-state pumping flow rate, thereby revealing the quantitative correlation between the two and the underlying regulatory mechanism. The results indicate that, within the experimental deformation range, the pumping flow rate of the four-chamber peristaltic pump exhibits a quasi-linear positive correlation with the maximum deformation intensity of the magnetic membrane, with a coefficient of determination R² > 0.99; when the deformation intensity exceeds a critical threshold, the rate of flow increase slows down and gradually approaches saturation. This simulation study achieved quantitative verification of the relationship between magnetic membrane deformation and flow rate without the need for experiments, providing a theoretical basis and data support for the precise flow control and structural parameter optimization of four-chamber magnetically driven peristaltic pumps.

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The principle of uncertainty is one of the essential principles of quantum mechanics as it outlines that there is a reflective limit to the measurement of microscopic particle motion. However it is impossible to measure at once and accurately both the position and momentum data of any microscopic particle no matter what means are used to do so. This paper starts by discussing the notion and mathematical derivation of the uncertainty principle as put forth by Heisenberg explaining the physical relevance of the principle. It introduces the natural uncertainty the principle introduces to the dynamics of particles in the microscopic world, and the significant part it plays in the theory of quantum mechanics. The article overviews the explanations of the principle on phenomena including atomic stability and scanning tunneling microscopes, and the interference of measurement actions on the states of particles and the shortcomings of measurement precision. According to the wave-particle duality of quantum mechanics, this article gives a required theoretical basis of microscopic phenomena. Likewise, the use of the research theory on the uncertainty principle by distinguished scholars in the physics fraternity helps the readers in achieving the knowledge in related subjects more easily.

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