Search

This paper proposes the BP-CM teaching model to solve the problems of lagging classroom feedback, poor learning initiative of students and students rapidly forgetting what they have learned in Internet of Things (IoT) hardware technology courses. This BP-CM(BOPPPS, PAD, cyclic memory, memory system) teaching model is based on BOPPPS(Bridge in, Objective, Pre-assessment, Participatory learning, Post-assessment, and Summary), PAD(Presentation, Assimilation, and Discussion), and the memory system model. This paper compares the BP-CM teaching model with the traditional teaching model by having research participants teach and learn separately in each teaching model. Taking the Microcontroller Unit (MCU) course as an example, this paper introduces each link of the teaching design of this model. It also explains the design method and intention of each link. Finally, the practical results indicate that the BP-CM teaching model proposed in this article positively impacts students' learning initiative and memory. In addition, teachers can obtain timely feedback using the BP-CM model, and the model significantly improves students' knowledge and ability levels. It enhances the teaching quality of IoT hardware technology courses and is conducive to cultivating students' sustainable learning abilities. This research not only promotes education and teaching reform but also provides a reference case.

Emotion recognition is an advanced technology for understanding human behavior and psychological states, with extensive applications for mental health monitoring, human–computer interaction, and affective computing. Based on electroencephalography (EEG), the biomedical signals naturally generated by the brain, this work proposes a resource-efficient multi-entropy fusion method for classifying emotional states. First, Discrete Wavelet Transform (DWT) is applied to extract five brain rhythms, i.e., delta, theta, alpha, beta, and gamma, from EEG signals, followed by the acquisition of multi-entropy features, including Spectral Entropy (PSDE), Singular Spectrum Entropy (SSE), Sample Entropy (SE), Fuzzy Entropy (FE), Approximation Entropy (AE), and Permutation Entropy (PE). Then, such entropies are fused into a matrix to represent complex and dynamic characteristics of EEG, denoted as the Brain Rhythm Entropy Matrix (BREM). Next, Dynamic Time Warping (DTW), Mutual Information (MI), the Spearman Correlation Coefficient (SCC), and the Jaccard Similarity Coefficient (JSC) are applied to measure the similarity between the unknown testing BREM data and positive/negative emotional samples for classification. Experiments were conducted using the DEAP dataset, aiming to find a suitable scheme regarding similarity measures, time windows, and input numbers of channel data. The results reveal that DTW yields the best performance in similarity measures with a 5 s window. In addition, the single-channel input mode outperforms the single-region mode. The proposed method achieves 84.62% and 82.48% accuracy in arousal and valence classification tasks, respectively, indicating its effectiveness in reducing data dimensionality and computational complexity while maintaining an accuracy of over 80%. Such performances are remarkable when considering limited data resources as a concern, which opens possibilities for an innovative entropy fusion method that can help to design portable EEG-based emotion-aware devices for daily usage. [ABSTRACT FROM AUTHOR]

In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model is applied for sentiment classification of those keyword-generated images. For method validation, the data randomly comprised of 5000 reviews from Amazon have been analyzed. With superior keyword extraction capability, the proposed method achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%. Such performance demonstrates its advantages by using the text-to-image approach, providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works. Thus, the proposed method enhances the reliability and insights of customer feedback surveys, which would also establish a novel direction in similar cases, such as social media monitoring and market trend research. [ABSTRACT FROM AUTHOR]

The acid detergent fiber (ADF) from tobacco leaves was obtained by treating the sample with petroleum ether-ethanol (6:4, v/v), 30 g/L sodium dodecylsulfate and 0.5 mol/L sulphuric acid containing 20 g/L hexadecyl trimethyl ammonium bromide successively. The ADF was degraded by the alkaline CuO oxidation procedure. In this work, six samples of ADF degradation products from tobacco leaves were prepared. The samples were analyzed by using gradient liquid chromatography (LC) where an Ultimate XB C18 column was used as stationary phase, with a mixture of methanol and water as mobile phase, at a column temperature of 35 °C and a flow rate of 0.8 mL/min. Dual wavelengths of 280 nm and 320 nm were chosen for the detection. It was found that there were four characteristic peaks for the ADF degradation products. By taking these peaks as research object, the optimum time for the degradation was found to be 5 h and the sample solution could be kept stable within 7 days. The established method may provide a new approach for the studies of the differences between lignin composition in different tobacco leaves and the relationship between lignin content and the smoking quality of tobacco leaves.