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Background: In 2020, the WHO proposed the global strategic goal of accelerating the elimination of cervical cancer (CC). One of the key strategies is that, by 2030, 90% of girls will complete HPV vaccination by age 15. In 2017, HPV vaccines were first marketed in China. Objectives: This study aimed to explore the changes in parents' knowledge about CC, HPV, HPV vaccines, and acceptance of HPV vaccination 5 years after the introduction of HPV vaccines into China. Associated factors and reasons for refusal by parents were also explored. Methods: A school-based follow-up study was conducted among parents in China from May 2018 to May 2023. Comparison using Chi-square tests was performed to measure the changes. Univariate and multivariate analyses were used to explore factors associated with HPV vaccination among teenage girls. Results: The overall cognitive level in terms of CC- and HPV-related knowledge among parents increased, and they expressed more willingness to vaccinate their daughter (35.4% in 2018 vs. 56.6% in 2023). The HPV vaccination rate among teenage girls remained at a low level (4.4% in 2018, 9.8% in 2023). The main obstacles reported by parents were limited knowledge (42.0%), scarcity of the HPV vaccine (29.2%), waiting until their daughter was older (27.5%), safety concerns (14.7%), high cost (9.0%), and effectiveness concerns (4.6%). Parents who are elderly, local residents, have received HPV vaccines themselves, have had experience of vaccinating their children with influenza vaccines, and have a higher knowledge level of CC, HPV and HPV vaccines are more likely to vaccinate their children with HPV vaccines. Conclusions: Although we observed an increment in parents' knowledge level in terms of CC, HPV, HPV vaccines, and HPV vaccine uptake over the past 5 years, the HPV vaccine coverage among girls still falls short of the WHO's 2030 target. Comprehensive intervention strategies, including tailored health education among adolescents and their parents, doctor recommendations, and providing financial subsidies or free HPV vaccines are needed in China. [ABSTRACT FROM AUTHOR]

SUMMARY: Maize is one of the world's most important staple crops, yet its production is increasingly threatened by the rising frequency of high‐temperature stress (HTS). To investigate the genetic basis of anther thermotolerance under field conditions, we performed linkage and association analysis to identify HTS response quantitative trait loci (QTL) using three recombinant inbred line (RIL) populations and an association panel containing 375 diverse maize inbred lines. These analyses resulted in the identification of 16 co‐located large QTL intervals. Among the 37 candidate genes identified in these QTL intervals, five have rice or Arabidopsis homologs known to influence pollen and filament development. Notably, one of the candidate genes, ZmDUP707, has been subject to selection pressure during breeding. Its expression is suppressed by HTS, leading to pollen abortion and barren seeds. We also identified several additional candidate genes potentially underly QTL previously reported by other researchers. Taken together, our results provide a pool of valuable candidate genes that could be employed by future breeding programs aiming at enhancing maize HTS tolerance. Significance Statement: Our research will provide a pool of valuable candidate genes that could be employed by future breeding programs aiming at enhancing maize HTS tolerance. [ABSTRACT FROM AUTHOR]

The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance. [ABSTRACT FROM AUTHOR]

Accurate identification and monitoring of aircraft on the airport surface can assist managers in rational scheduling and reduce the probability of aircraft conflicts, an important application value for constructing a "smart airport." For the airport surface video monitoring, there are small aircraft targets, aircraft obscuring each other, and affected by different weather, the aircraft target clarity is low, and other complex monitoring problems. In this paper, a lightweight model network for video aircraft recognition in airport field video in complex environments is proposed based on SSD network incorporating coordinate attention mechanism. First, the model designs a lightweight feature extraction network with five feature extraction layers. Each feature extraction layer consists of two modules, Block_A and Block_I. The Block_A module incorporates the coordinate attention mechanism and the channel attention mechanism to improve the detection of obscured aircraft and to enhance the detection of small targets. The Block_I module uses multi-scale feature fusion to extract feature information with rich semantic meaning to enhance the feature extraction capability of the network in complex environments. Then, the designed feature extraction network is applied to the improved SSD detection algorithm, which enhances the recognition accuracy of airport field aircraft in complex environments. It was tested and subjected to ablation experiments under different complex weather conditions. The results show that compared with the Faster R-CNN, SSD, and YOLOv3 models, the detection accuracy of the improved model has been increased by 3.2%, 14.3%, and 10.9%, respectively, and the model parameters have been reduced by 83.9%, 73.1%, and 78.2% respectively. Compared with the YOLOv5 model, the model parameters are reduced by 38.9% when the detection accuracy is close, and the detection speed is increased by 24.4%, reaching 38.2fps, which can well meet the demand for real-time detection of aircraft on airport surfaces. [ABSTRACT FROM AUTHOR]

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, which leads to generated results failing to achieve a high identity similarity to the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllably transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images, and controllable face swapping. [ABSTRACT FROM AUTHOR]