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Measuring the distribution of the fishing effort of trawlers is of great significance for describing marine fishery activities, quantifying fishing systems in terms of marine ecological pressure, and revising the regulations of fishing. The purpose of this paper is to develop an efficient learning algorithm to detect the fishing behavior of trawlers to analyze the distribution of fishing effort. The vessel-monitoring system data of more than 4600 trawlers from September 2019 to April 2023 were used for feature extraction. According to the spatiotemporal information provided by the vessel position data, 11-dimensional features were extracted to form the feature vectors. A Slime Mould Algorithm-optimized Light Gradient-Boosting Machine (SMA-LightGBM) algorithm was proposed to classify the feature vectors to recognize fishing behavior. The presented method showed a remarkable generalization ability and high accuracy, sensitivity, specificity, and Matthews correlation coefficient in the test results, with scores of 98.23%, 98.75%, 97.75%, and 0.9646, respectively. Subsequently, the trained model was used to identify the fishing behavior of trawlers belonging to the coastal provinces of the Bohai Sea and the Yellow Sea in the sea area of 117° E~132° E, 26° N~41° N. The fishing effort was calculated and evaluated according to the fishing behavior recognition results. The mean absolute error was 0.3031 kW·h, and the coefficient of determination score was 0.9772. The thermal map of the fishing effort of the trawler was mapped, and the spatiotemporal characteristics were estimated in the region of interest from 2019 to 2023 with a spatial resolution of 18 degree × 18 degree. This method is an efficient way of analyzing the spatiotemporal characteristics of the fishing effort of trawlers. It provides a quantitative basis for the assessment of fishery resources and can inform fishing policies.

The efficiency of the fabrication and the cost of feedstock materials are important constraining factors for a wider application of the laser powder bed fusion (LPBF) process in the industry. Therefore, it is necessary to investigate the feasibility of using different particle size distributions (PSD) combined with higher layer thickness for achieving higher building efficiency and cost-effectiveness. This paper focuses on the effect of PSD (0–53, 15–53, 15–75, and 15–105 μm) on the print quality and mechanical properties of the LPBF-processed Ti-6Al-4V at a layer thickness of 60 μm. The results show that volumetric energy density (VED) range, which allows the coarse powder to reach full density, becomes relatively narrower but is still capable of producing fully dense parts when the parameters are properly adjusted. Among the fully dense specimens, the surface roughness varies slightly with the increase of VED and PSD. In the case of proper parameter selection, specimens made of coarse powder can still achieve low surface roughness. Only slight differences in mechanical performance are found for specimens produced using different PSD powders as they have almost identical microstructures. The issue of the anisotropic mechanical properties of the as-built specimens is resolved after annealing treatment at 800 °C for 2 h. This study provides a guideline for producing high-quality Ti-6Al-4V parts using a higher layer thickness and coarser powders.