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The purpose of this study is to evaluate online-merge-offline (OMO)-based music therapy (MT) as a complementary option for asthma management in pediatric patients. A total of 86 children diagnosed with mild asthma were enrolled and treated with the same drug therapy. They were assigned into three groups: Music I group (standard medical care plus a single individualized MT session along with singing training and breathing exercise), Music II group (similar as Music I as well as further wind instrument playing), and Control group (standard medical care). Primary endpoints included pulmonary function tests FEV1, FVC, FEV1/FVC, MMEF 75/25, and PEF, c-ACT, PAQLQ, and PACQLQ. After 6 months of continuous intervention of MT, significant differences in FEV1, FVC, MMEF75/25, PEF, c-ACT score, PAQLQ, PACQLQ (p < 0.001), and FEV1/FVC (p < 0.05) were observed among Music I, Music II, and Control groups. Besides, FEV1, FVC, FEV1/FVC, MMEF75/25, and PEF showed positive trends in Music I and Music II groups compared to those in Control group (p < 0.05). The c-ACT score of children was significantly increased in Music I (p < 0.001) and II (p < 0.001) groups in contrast with Control group. Children in Music I and II groups had better quality of life than those in Control group (PAQLQ, p < 0.001), and the parents in Music I and II groups also showed better quality of life than those in Control group (PACQLQ, p < 0.001). Conclusion: As a child-friendly, low-risk, and convenient intervention, the OMO-based MT has a positive impact on pediatric asthma management during the COVID-19 pandemic. What is Known: • A few findings proved the positive effect of MT on pediatric asthma. What is New: • Our study further proving the validation and effectiveness of MT with OMO-based model on pediatric asthma, wind instrument playing has a greater impact on pediatric asthma control via small airways and might be recommended to mix to singing and breathing to improve effectiveness of MT for asthmatic children. [ABSTRACT FROM AUTHOR]

We propose an interactive analytical system for exploring and interpreting non-linear projections. Although non-linear projections are widely used in disclosing complex structures in high-dimensional analysis, there is a strong need to interpret them due to their inherent complexity and weak interpretability. In the machine learning and visualization communities, it is inspiring to use local linear models to fit and interpret non-linear models. Regarding non-linear projections, there are research gaps in both generation and exploration of linear fitting segments. To fill this gap, we propose an optimization algorithm to partition a non-linear projection into linear segments according to the feature of local affine transformations. We then construct a hierarchy of linear segments and conjunct hierarchical visualizations to support a coarse-to-fine exploration. After that, we design and implement a visual interface that integrates the proposed algorithms and a suite of visual tools. Three case studies demonstrate that the proposed approach facilitates the interpretation of non-linear projections. [ABSTRACT FROM AUTHOR]