Search

Accurate measurement of frontomaxillary facial (FMF) angles in prenatal ultrasound (US) scans plays a pivotal role in the screening of trisomy 21. Nevertheless, this intricate procedure heavily relies on the proficiency of the ultrasonographer and tends to be a time-intensive task. Furthermore, FMF angles are subjective when measured manually. To address this challenge, we propose a deep learning-based assisted examination framework for automatically measuring FMF angles on 2D ultrasound images. Firstly, we trained a deep learning network using 1549 fetal ultrasound images to achieve automatic and accurate segmentation of critical areas. Subsequently, a key point detection network was employed to predict the coordinates of the requisite points for calculating FMF angles. Finally, FMF angles were obtained through computational means. We employed Pearson correlation coefficients and Bland–Altman plots to assess the correlation and consistency between the model's predictions and manual measurements. Notably, our method exhibited a mean absolute error of 2.354°, outperforming the typical standards of the junior expert. This indicates the high degree of accuracy and reliability achieved by our approach. [ABSTRACT FROM AUTHOR]

Ecological restoration projects aim to recover ecosystems that have been degraded, damaged, or destroyed. The effective assessment of these projects is crucial for ensuring that restoration goals are met and for guiding future efforts. The Poplar Ecological Restoration Project (PERP), focused on removing floodplain poplar plantations, is a key environmental initiative in China to preserve the ecological functions and services of the Dongting Lake freshwater wetland complex. Understanding the dynamic changes in ecosystem services and their trade-offs/synergies before and after the project is crucial for ensuring that ecosystem services are maintained and enhanced. This study selected three key ecosystem services, water conservation, carbon storage, and habitat quality, as indicators to assess the PERP. Using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and geostatistical methods, including the Spearman correlation analysis and spatial autocorrelation analysis, we explored the ecological outcomes of the project. The results indicated that since its implementation in 2014, the PERP has significantly altered the land use and land cover (LULC) in the lake. Most of the former plantations have been converted into reedbeds (17,404 ha), followed by sedgelands (3841 ha), mudflats (443 ha), shallow waters (382 ha), and deep waters (301 ha). During this period, water conservation and habitat quality initially decreased and then increased, while carbon storage continuously declined. Overall, all assessed services showed an increasing trend except for carbon storage, resulting in a slight decrease in the integrated ecosystem services. Moreover, there were strong bivariate associations among the three investigated ecosystem services, and the synergies were strengthened during the project. Importantly, these synergies became positive after the PERP, meaning that the improvement of one service enhanced others, potentially leading to a more sustainable and resilient ecosystem. [ABSTRACT FROM AUTHOR]

Fetal cleft lip is a common congenital defect. Considering the delicacy and difficulty of observing fetal lips, we have utilized deep learning technology to develop a new model aimed at quickly and accurately assessing the development of fetal lips during prenatal examinations. This model can detect ultrasound images of the fetal lips and classify them, aiming to provide a more objective prediction for the development of fetal lips. This study included 632 pregnant women in their mid-pregnancy stage, who underwent ultrasound examinations of the fetal lips, collecting both normal and abnormal fetal lip ultrasound images. To improve the accuracy of the detection and classification of fetal lips, we proposed and validated the Yolov5-ECA model. The experimental results show that, compared with the currently popular 10 models, our model achieved the best results in the detection and classification of fetal lips. In terms of the detection of fetal lips, the mean average precision (mAP) at 0.5 and mAP at 0.5:0.95 were 0.920 and 0.630, respectively. In the classification of fetal lip ultrasound images, the accuracy reached 0.925. The deep learning algorithm has accuracy consistent with manual evaluation in the detection and classification process of fetal lips. This automated recognition technology can provide a powerful tool for inexperienced young doctors, helping them to accurately conduct examinations and diagnoses of fetal lips. [ABSTRACT FROM AUTHOR]

The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, the challenge of addressing the VRPTW with many conflicting objectives (MOVRPTW) still remains for DRL. The MOVRPTW considers five conflicting objectives simultaneously: minimizing the number of vehicles required, the total travel distance, the travel time of the longest route, the total waiting time for early arrivals, and the total delay time for late arrivals. To tackle the MOVRPTW, this study introduces the MTMO/DRP-AT, a multi-task multi-objective evolutionary search algorithm, by making full use of both DRL and the multitasking mechanism. In the MTMO/DRL-AT, a two-objective MOVRPTW is constructed as an assisted task, with the objectives being to minimize the total travel distance and the travel time of the longest route. Both the main task and the assisted task are simultaneously solved in a multitasking scenario. Each task is decomposed into scalar optimization subproblems, which are then solved by an attention model trained using DRL. The outputs of these trained models serve as the initial solutions for the MTMO/DRL-AT. Subsequently, the proposed algorithm incorporates knowledge transfer and multiple local search operators to further enhance the quality of these promising solutions. The simulation results on real-world benchmarks highlight the superior performance of the MTMO/DRL-AT compared to several other algorithms in solving the MOVRPTW. [ABSTRACT FROM AUTHOR]