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In the era of inclusive education, students with attention deficits are integrated into the general classroom. To ensure a seamless transition of students' focus towards the teacher's instruction throughout the course and to align with the teaching pace, this paper proposes a continuous recognition algorithm for capturing teachers' dynamic gesture signals. This algorithm aims to offer instructional attention cues for students with attention deficits. According to the body landmarks of the teacher's skeleton by using vision and machine learning-based MediaPipe BlazePose, the proposed method uses simple rules to detect the teacher's hand signals dynamically and provides three kinds of attention cues (Pointing to left, Pointing to right, and Non-pointing) during the class. Experimental results show the average accuracy, sensitivity, specificity, precision, and F1 score achieved 88.31%, 91.03%, 93.99%, 86.32%, and 88.03%, respectively. By analyzing non-verbal behavior, our method of competent performance can replace verbal reminders from the teacher and be helpful for students with attention deficits in inclusive education. [ABSTRACT FROM AUTHOR]

Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining clear underwater images has been a challenge, because scattering and blur hinder the rendering of true underwater colors, affecting the accuracy of underwater exploration. Therefore, this paper proposes a new deep network model for single underwater image enhancement. More specifically, our framework includes a light field module (LFM) and sketch module, aiming at the generation of a light field map of the target image for improving the color representation and preserving the details of the original image by providing contour information. The restored underwater image is gradually enhanced, guided by the light field map. The experimental results show the better image restoration effectiveness, both quantitatively and qualitatively, of the proposed method with a lower (or comparable) computing cost, compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Nitrated phenols are a group of nitrogen-containing organics ubiquitously present in ambient air, which are also important components of atmospheric light-absorbing organic matter (brown carbon) that have significant impacts on climate change, air quality, and human health. In this study, we collected a total of 265 daily filter samples of fine particles (PM 2.5 ) in northern suburban Nanjing from March 2019 to January 2020. We used ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) to detect and quantify eight nitrated phenolic species. The results showed that the average annual concentration of total nitrated phenols in the sampling site was 18.77 ng·m -3 , and the average concentrations in spring, summer, autumn, and winter were 16.82, 8.59, 17.28, and 44.79 ng·m -3 , respectively. Such concentrations were obviously higher than those determined in other countries but were similar to those in domestic cities, such as Jinan. 4-Nitrophenol was the most abundant nitrated phenol, followed by 4-nitrocatechol and 2-methoxy-5-nitrophenol. Correlation analysis showed that 3-nitrosalicylic acid was from a specific source different from that of other species. Finally, we used a positive matrix factorization model to quantify the source contributions of nitrated phenols. The major sources were vehicle emissions (32%), mixed coal and biomass burning emissions (44%), and industrial emissions (24%). The mixed coal and biomass burning emissions were dominant in autumn and winter. The mass fraction of 3-nitrosalicylic acid in the factor of industrial emissions was>90%, consistent with the results of the correlation analysis. Overall, this study provides valuable insights into the understanding of concentrations, characteristics, and sources of atmospheric nitrated phenols in ambient air.