We aimed to comprehensively examine the association of breast-feeding, types and initial timing of complementary foods with adolescent cognitive development in low- and middle-income countries. We conducted a prospective cohort study of 745 adolescents aged 10–12 years who were born to women who participated in a randomised trial of prenatal micronutrient supplementation in rural Western China. An infant feeding index was constructed based on the current WHO recommendations. Full-scale intelligence quotient (FSIQ) was assessed and derived by the fourth edition of the Wechsler Intelligence Scale for Children. The duration of exclusive or any breast-feeding was not significantly associated with adolescent cognitive development. Participants who regularly consumed Fe-rich or Fe-fortified foods during 6–23 months of age had higher FSIQ than those who did not (adjusted mean differences 4·25; 95 % CI 1·99, 6·51). For cows’/goats’ milk and high protein-based food, the highest FSIQ was found in participants who initially consumed at 10–12 and 7–9 months, respectively. A strong dose–response relationship of the composite infant feeding index was also identified, with participants in the highest tertile of overall feeding quality having 3·03 (95 % CI 1·37, 4·70) points higher FSIQ than those in the lowest tertile. These findings suggest that appropriate infant feeding practices (breast-feeding plus timely introduction of appropriate complementary foods) were associated with significantly improved early adolescent cognitive development scores in rural China. In addition, improvement in Fe-rich or Fe-fortified foods complementary feeding may produce better adolescent cognitive development outcomes.
Rapid and accurate assessment of the damage to bridge structures after an earthquake can provide a basis for decision-making regarding post-earthquake emergency work. However, the traditional structural damage inspection techniques are subjective, time-consuming, and inefficient. This paper proposed a framework for rapid post-earthquake structural damage inspection and condition assessment by integrating the technologies of satellite, unmanned aerial vehicle (UAV), and smartphone with the deep learning approach. The images of structural components of post-earthquake bridges can be obtained by UAVs and smartphones. Furthermore, the multi-task high-resolution net (MT-HRNet) model was adopted to recognize the structural components and damage conditions by weighting and combining the loss functions of a single-task HRNet model. The performance of the proposed MT-HRNet model and the single-task HRNet model was verified based on the Tokaido dataset, which includes 2000 images of post-earthquake bridges. The results showed that the MT-HRNet model and the HRNet model exhibited equivalent recognition accuracy, while the number of floating-point-operations (FLOPs) and the parameters of the MT-HRNet model were reduced by 46.48% and 49.58% compared with the HRNet model. In addition, a method for the determination of the safety risk level of the post-earthquake bridge structures was developed, and the evaluation indices were established by considering the damage type, the spalling area, and the width of cracks as well as the recognition statistics of all images in Tokaido dataset. This study will provide a valuable reference for the rapid determination of structural safety level and the corresponding treatment measures of post-earthquake bridges.
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