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Determining how mountain communities in conservation priority areas can benefit from rich local biodiversity through sustainable development has been a challenge to governments throughout the world. The village of Hanlong in western Yunnan China has been developing bird photography tourism to capitalize on the extremely rich avifaunal diversity of the area. In this study, we analyzed the impact of the emerging tourism on local communities' livelihoods and identified the key areas of improvements for wider dissemination of the experiences. Bird photography tourism in Hanlong has contributed significantly to the sustainable livelihoods of local communities by providing a major new source of income, creating local employment opportunities, and reducing local people's dependence on the extractive use of forest resources. In the process, local communities have acquired new knowledge, skills, and social networks. They have also developed new institutions and governance mechanisms, which enable them to better adapt to the changing socioeconomic environment. Bird photography tourism offers new opportunities and hope for sustaining local livelihoods and biodiversity in conservation priority areas. Experiences in Hanlong offer an excellent heuristic example for sustainable community development, adaptation, and transformational change in conservation priority areas. They also contribute to the realization of the Sustainable Development Goals and Aichi targets and thus have value for wider upscaling. Geographical location, local leadership, and external support are all important for the success of bird photography in Hanlong. We recommend that a landscape (including transboundary landscape) approach be adopted to integrate farming practices, other economic activities, and photography tourism. Farming practices and land uses that sustain bird diversity should be encouraged.

Cognitive engagement involves mental and physical involvement, with observable behaviors as indicators. Automatically measuring cognitive engagement can offer valuable insights for instructors. However, object occlusion, inter-class similarity, and intra-class variance make designing an effective detection method challenging. To deal with these problems, we propose the Object-Enhanced–You Only Look Once version 8 nano (OE-YOLOv8n) model. This model employs the YOLOv8n framework with an improved Inner Minimum Point Distance Intersection over Union (IMPDIoU) Loss to detect cognitive engagement. To evaluate the proposed methodology, we construct a real-world Students' Cognitive Engagement (SCE) dataset. Extensive experiments on the self-built dataset show the superior performance of the proposed model, which improves the detection performance of the five distinct classes with a precision of 92.5%. [ABSTRACT FROM AUTHOR]

Background: Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID-19 pandemic. However, online learning has seen students lose interest and become anxious, which affects learning performance and leads to dropout. Thus, measuring students' engagement in online learning has become imperative. It is challenging to recognize online learning engagement due to the lack of effective recognition methods and publicly accessible datasets. Methods: This study gathered a large number of online learning videos of students at a normal university. Engagement cues were used to annotate the dataset, which was constructed with three levels of engagement: low engagement, engagement, and high engagement. Then, we introduced a bi-directional long-term recurrent convolutional network (BiLRCN) for online learning engagement recognition in video. Result: An online learning engagement dataset has been constructed. We evaluated six methods using precision and recall, where BiLRCN obtained the best performance. Conclusions: Both category balance and category similarity of the data affect the performance of the results; it is more appropriate to consider learning engagement as a process-based evaluation; learning engagement can provide intervention strategies for teachers from a variety of perspectives and is associated with learning performance. Dataset construction and deep learning methods need to be improved, and learning data management also deserves attention. [ABSTRACT FROM AUTHOR]

A new spectral‐spatial hyperspectral image (HSI) classification method called hierarchical broad learning system (HBLS) has been proposed in this paper. Specifically, it combines wavelet, broad learning system (BLS) and Gabor filters into a hierarchical structure. First of all, wavelet is used to reduce the observation noise of HSIs. Then BLS is adopted to acquire a set of pixelwise probability maps from the input data, and Gabor filters are used to explore spatial information by refining these probability maps. These two operations (BLS and Gabor filtering) are alternated to form a hierarchical architecture. And the discriminative spectral‐spatial features can be extracted at each layer of the hierarchical architecture. Finally, the spectral‐spatial features are fed into the standard BLS for classification. Experimental results on three widely used HSIs reveal that HBLS outperforms some state‐of‐the‐art methods in terms of classification accuracy and sample complexity. [ABSTRACT FROM AUTHOR]