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Studies have highlighted the possible advantages of tangible user interfaces (TUIs) over multitouch interfaces (MTIs) in preschool education; however, more objective evidence is required to establish the superiority of TUIs. In this article, we design a mathematical application called THE NUMBERS to compare preschool children's learning potential and employed the eye-tracking method to assess the cognitive differences between the TUI and MTI versions. The results demonstrate that the TUI version participants made more attempts in the task, which was a significant predictor of learning outcomes. Furthermore, using TUIs leads to lower cognitive load, increases attention on key areas, and provides higher entertainment value, which has long-term effects on promoting preschool children's learning. The results of the perception questionnaire show that the TUI version is easier to use, more interesting, and more motivating for them to learn mathematics than the MTI version is. On the other hand, the interview results show that 62.5% of students prefer to use the TUI version to learn mathematics. The results can provide educators with a deeper understanding of the mental characteristics involved in MTIs and TUIs to enable them to design educational applications that are more suitable for preschool children.

BackgroundIncreasing evidence shows that alterations in gut microbiome (GM) contribute to the development of type 2 diabetes mellitus (T2DM), and fecal microbiota transplantation (FMT) successfully treats various human diseases. However, the benefits of FMT therapy to T2DM patients remain unknown.MethodsWe enrolled 17 patients with T2DM for nonblinded, one-armed intervention trial of FMT. A total of 20 healthy individuals were recruited as the baseline control. HbA1c% and metabolic parameter change were evaluated in 17 T2DM patients 12 weeks after they received FMT from healthy donors. The GM composition was characterized by 16S rRNA gene amplicon sequencing from fecal samples prior to and 12 weeks after FMT treatment.ResultsWe found that the GM of T2DM patients was reconstituted by FMT. We observed a statistically significant decrease in HbA1c% (from 7.565 ± 0.148 to 7.190 ± 0.210, p

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision–Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method's heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure. [ABSTRACT FROM AUTHOR]

The functionalized polymer with scalable properties presents a promising option for flexible triboelectric nanogenerators designed for detecting human motion. In this study, a novel method to fabricate tribopositive composite film was proposed by incorporating high permittivity N-doped orange peel-based carbon dots (O-CDs) into the highly cross-linked polyvinyl alcohol (PVA). The O-CDs/PVA sensor showed elevated electrical output with the raised content of O-CDs until 1.5 wt. % with a peak voltage of 3.5 V. The response characteristics under various external force conditions were tested, and the maximum peak voltage and current reached 3.92 V and 28.6 A under the force of 50 N. Moreover, O-CDs/PVA sensors present superior high-output stability, durability, and accurate distinction to different movement states, even the vocal cord vibration. By introducing the support vector machine learning algorithm, intelligent gesture recognition was achieved. This work paves a facile, feasible, and scalable pathway to the self-powered human motion sensor device. [ABSTRACT FROM AUTHOR]