The vehicle routing problem (VRP) is a common problem in logistics and transportation with high application value. In the past, many methods have been proposed to solve the vehicle routing problem and achieved good results, but with the development of neural network technology, solving the VRP through neural combinatorial optimization has attracted more and more attention by researchers because of its short inference time and high parallelism. PMOCO is the most state-of-the-art multi-objective vehicle routing optimization algorithm. However, in PMOCO, preferences are often uniformly selected, which may lead to uneven Pareto sets and may reduce the quality of solutions. To solve this problem, we propose a multi-objective vehicle routing optimization algorithm based on preference adjustment, which is improved from PMOCO. We incorporate the weight adjustment method in PMOCO that is able to adapt to different approximate Pareto fronts and to find solutions with better quality. We treat the weight adjustment as a sequential decision process and train it through deep reinforcement learning. We find that our method could adaptively search for a better combination of preferences and have strong robustness. Our method is experimented on multi-objective vehicle routing problems and obtained good results (about 6% improvement compared with PMOCO with 20 preferences).

Radio altimetry is important for sensing the near-ground altitude of airborne platforms operating at several-kilometre altitudes. In urban environments, multipath propagation and coherent superposition from buildings and ground surfaces can alter echo delay, energy, and waveform, thereby affecting downstream ranging and altitude-estimation decisions. Conventional echo models often cannot retain the geometric propagation delays, material properties, polarization transformations, and coherent phase relationships of individual scattering components in kilometer-scale three-dimensional scenes, limiting their ability to reconstruct physically consistent time-domain echoes. To address this, this study combines radio-altimetry echo simulation with electromagnetic simulation of urban environments and develops a finite-distance spherical-wave physical-optics method for modeling time-domain echoes in large-scale urban scenes. The method maps the scattering responses of urban surface facets to the received time-domain echo. Representative cases verify the accuracy of calculations of scattering amplitude, range attenuation, and two-way propagation delay. The results show that a uniform material assignment predominantly affects echo amplitude and energy while exerting only a limited influence on the delay structure. In contrast, variations in urban geometry alter the spatial locations and orientations of effective scattering facets and the coherence relationships among their complex fields, resulting in distinct main-peak positions, energy-delay distributions, and early- and late-arriving multipath structures.

Some materials will be hydrogenated to corrosion, such as metal materials, organic materials, polymer materials and so on, when the concentration of hydrogen in a mixed gas is greater than 4% in a closed space of micro environment. Their performances will be seriously affected. So removing or reducing the concentration of hydrogen in a micro environment can fundamentally solve the problem of hydrogenation corrosion. Metallic hydrogen removing agent and organic hydrogen removing agent are the main research directions of controlling materials of hydrogen concentration in micro environment. Current research status in the world are comprehensively reviewed from these two aspects. Features and application scopes of these two kinds of materials are pointed out separately, which provide a theoretical reference for the prevention of accidents caused by hydrogenation corrosion.

Due to the high density of warehouse distribution, port cargo is highly susceptible to fire, humidity, and various natural disasters. When these disasters occur, they can result in severe cargo losses. Meanwhile, transportation cost control has long been a prominent research focus in port logistics due to the enormous throughput. To address these problems, this paper proposes a two-stage allocation–transportation framework based on improved holistic swarm optimization. In the allocation stage, this paper designs a warehouse zoning strategy. Based on the distance criterion, a clustering method is employed to group spatially proximate warehouses into the same zone, and distribute the same cargo across different zones. In this way, the same cargo could be prevented from being completely destroyed in a disaster. In the transportation stage, this paper designs an improved holistic swarm optimization algorithm to plan the routes of cargo transportation. In this method, this paper integrates the greedy search algorithm, ant colony optimization, and adaptive swap/reversal operations with the holistic swarm optimization algorithm, which could enhance global search capability and significantly reduce the length of transportation routes. To validate the effectiveness of the proposed algorithm, experiments are conducted on both instances of varying scales and CVRPLIB benchmark instances, and the results are compared with several baseline methods. Specifically, compared with the best-performing baseline ACO, IHSO reduces the optimal route length by up to 2.08% on the self-generated instances and by 5.24% on the CVRPLIB benchmark instances. Moreover, all improvements are statistically significant under the Wilcoxon signed-rank test (p < 0.05), confirming that the advantage of IHSO is systematic rather than incidental. The proposed algorithm demonstrates high adaptability in solving port cargo path planning problems, delivering high-quality solutions.