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According to the lack of sea detection research and the high complexity of genetic algorithm (GA) based on actual terrain data in radar network deployment optimization, this paper carries out the research on seaside radar deployment optimization with the digital elevation model (DEM) data and genetic algorithm. Based on the given positions and seaside radar detection model, the proposed algorithm can greatly reduce the searching space of GA and improve the solving speed. Simulation results show that, our algorithm can obtain almost the same optimal results compared with the exhausted searching method, while enjoying obvious advantage in speeding with the increase of deployment scale.

A measurement method and system based on conventional radars has been proposed to address the problem of radar cross-section (RCS) measurement of ship targets in open seas. Calibration errors for RCS measurement on ground plane range (GPR) are analysed, and the error elimination method and accurate RCS calculation formula are given. An RCS measurement system is designed by improving conventional radars. These systems measure two Luneburg lens reflectors of different sizes on the lake. The average RCS of the big Luneburg lens reflector is obtained, and the error is calculated to be 2.73% compared with its actual value, which verifies the rationality of the proposed measurement method and system. The system measures the RCS of a ship in the open sea and obtains its circumferential RCS distribution. The effect of the error in the ship's position calculation on the RCS measurement results is analysed, and the uncertainty range of the error is obtained.

BACKGROUND: In agricultural pest management, especially in combatting the invasive red imported fire ant (RIFA, Solenopsis invicta), significant challenges emerge as a consequence of the constraints of solely depending on chemical insecticides or entomopathogenic nematodes (EPNs). The utilization of chemical insecticides carries environmental and ecological hazards, whereas EPNs, when applied independently, might not offer the immediate effectiveness necessary for adequate RIFA suppression. Acknowledging these hurdles, our study investigates a synergistic method that integrates EPNs with chemical insecticides, aiming to fulfill the urgent demand for more efficient and environmentally friendly pest control solutions. RESULTS: Our evaluation focused on the interaction between the highly pathogenic Steinernema riobrave 7–12 EPN strain and prevalent insecticides, specifically beta‐cypermethrin and a mixture of bifenthrin and clothianidin, applied at highly diluted recommended concentrations. The findings revealed a notable increase in RIFA mortality rates when EPNs and these insecticides were used together, outperforming the results achieved with each method individually. Remarkably, this enhanced efficacy was especially evident at lower concentrations of the bifenthrin–clothianidin mixture, indicating a valuable approach to minimizing reliance on chemical insecticides in agriculture. Furthermore, the high survival rates of EPNs alongside the tested insecticides indicate their compatibility and potential for sustained use in integrated pest management programs. CONCLUSION: Our research underscores the effectiveness of merging EPNs with chemical insecticides as a powerful and sustainable strategy for RIFA management. This combined approach not only meets the immediate challenges of pest control in agricultural settings, but also supports wider environmental objectives by reducing the dependency on chemical insecticides. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]