Influences of Adenoid Hypertrophy on Children's Maxillofacial Development.

This study aims to investigate the association between adenoid hypertrophy and facial development. A total of 388 children aged 1-13 years old who had undergone head MRI in Foshan Maternal and Child Health Hospital were collected, including 196 hypertrophic cases and 192 normal cases. The maxillofacial soft tissue indicators were measured and compared. The A/N ratio and adenoid thickness consistently increased with age in the hypertrophic group and the A/N ratio reached a maximum value three years earlier than the normal group. The pharyngeal airway space, vallecula of epiglottis to anterior plane distance of the third/fourth cervical vertebrae, angle of convexity, total angle of convexity, and the nasolabial angle in the hypertrophy group were smaller than those in the control group (p < 0.05). The thickness of adenoids, palate height, palate length, and tongue length in the hypertrophy group exceeded that of the control group (p < 0.05). To conclude, adenoid hypertrophy was associated with craniofacial features such as a convex facial profile, a narrowed nasopharyngeal airway, an elongated and heightened palate, a lengthened tongue or a lower tongue position. These findings emphasize the importance of early intervention for children with adenoid hypertrophy to mitigate potential adverse effects on maxillofacial development.

A Novel Memetic Algorithm Based on Multiparent Evolution and Adaptive Local Search for Large-Scale Global Optimization.

In many fields, including management, computer, and communication, Large-Scale Global Optimization (LSGO) plays a critical role. It has been applied to various applications and domains. At the same time, it is one of the most challenging optimization problems. This paper proposes a novel memetic algorithm (called MPCE & SSALS) based on multiparent evolution and adaptive local search to address the LSGO problems. In MPCE & SSALS, a multiparent crossover operation is used for global exploration, while a step-size adaptive local search is utilized for local exploitation. A new offspring is generated by recombining four parents. In the early stage of the algorithm execution, global search and local search are performed alternately, and the population size gradually decreases to 1. In the later stage, only local searches are performed for the last individual. Experiments were conducted on 15 benchmark functions of the CEC'2013 benchmark suite for LSGO. The results were compared with four state-of-the-art algorithms, demonstrating that the proposed MPCE & SSALS algorithm is more effective.