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Large datasets are required to develop Artificial Intelligence (AI) models in AI powered smart farming for reducing farmers' routine workload, this paper contributes the first large lion-head goose dataset GooseDetectlion, which consists of 2,660 images and 98,111 bounding box annotations. The dataset was collected with 6 cameras deployed in a goose farm in Chenghai district of Shantou city, Guangdong province, China. Images sampled from videos collected during July 9 -10 in 2022 were fully annotated by a team of fifty volunteers. Compared with another 6 well known animal datasets in literature, our dataset has higher capacity and density, which provides a challenging detection benchmark for main stream object detectors. Six state-of-the-art object detectors have been selected to be evaluated on the GooseDetectlion, which includes one two-stage anchor-based detector, three one-stage anchor-based detectors, as well as two one-stage anchor-free detectors. The results suggest that the one-stage anchor-based detector You Only Look Once version 5 (YOLO v5) achieves the best overall performance in terms of detection precision, model size and inference efficiency. [ABSTRACT FROM AUTHOR]

With the continuous development of artificial intelligence, more and more human–computer interaction (HCI) applications have begun to appear in the field of education. This article investigates the relationship between the HCI and cognitive load and academic emotion by comparing the effect of classroom discussion in the smart classroom with that of traditional classroom discussion and classroom discussion that focuses too much on learning interest. By using classroom test questionnaire and interview questionnaire, the experiment counted the test scores and questionnaire satisfaction of participants in different classroom discussions, and obtained the learning effect and the emotional state and satisfaction degree of learners under different classroom discussions. It is concluded that the artificial intelligence in the HCI of smart classroom should always take the amount of cognitive load of learners as the core, improve the academic emotion of learners as the auxiliary, reduce the cognitive load of learners as much as possible, and improve the academic emotion of learners within a reasonable range, so as to improve the learning effect. It also provides methods and suggestions for how to reduce learners' cognitive load and how to reasonably improve their academic emotions in smart classroom. [ABSTRACT FROM AUTHOR]

The demand for controllable fragrance materials is substantial owing to their potential to impart enduring scents in a variety of applications. However, the practical application of such materials has been limited by challenges in tunable morphogenesis, structural variability, and adaptability to diverse conditions. In our study, we introduce a hybrid living material that integrates a genetically engineered strain of Kluyveromyces marxianus CBS6556 with an adaptive hydrogel. The engineered K. marxianus achieved temperature stability in 2-phenylethanol (2-PE) and 2-phenylethyl acetate (2-PEAc) production by expressing relevant genes in the 2-PE metabolic pathway using the high-temperature preferential promoter SSE1. The enhanced water retention capacity supports the metabolic activities of the encapsulated yeast cells, ensuring their survival and functionality over an extended period. Fragrance-releasing living material (FLM) is designed to controllably emit fragrance 2-PE by adjusting the microbial concentration within the hydrogel matrix. The FLM exhibits versatile adhesion capabilities, effectively binding to a spectrum of surfaces such as wood, textiles, and glass as well as to natural substrates like leaves. This adaptability enhances the material's applicability across various settings. Furthermore, FLM can be crafted into various forms, including microbeads, fibers, and films. This research opens up new horizons for controlled fragrance release of living materials.