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This paper presents a novel approach to mind-wandering prediction in the context of webcam-based online learning. We implemented a Singular Value Decomposition (SVD)-based 1D temporal eye-signal extraction method, which relies solely on eye landmark detection and eliminates the need for gaze tracking or specialized hardware, then extract suitable features from the signals to train the prediction model. Our thorough experimental framework facilitates the evaluation of our approach alongside baseline models, particularly in the analysis of temporal eye signals and the prediction of attentional states. Notably, our SVD-based signal captures both subtle and major eye movements, including changes in the eye boundary and pupil, surpassing the limited capabilities of eye aspect ratio (EAR)-based signals. Our proposed model exhibits a 2% improvement in the overall Area Under the Receiver Operating Characteristics curve (AUROC) metric and 7% in the F1-score metric for 'not-focus' prediction, compared to the combination of EAR-based and computationally intensive gaze-based models used in the baseline study These contributions have potential implications for enhancing the field of attentional state prediction in online learning, offering a practical and effective solution to benefit educational experiences.
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We present the in situ synthesis of silver nanoparticles (AgNPs) through ionotropic gelation utilizing the biodegradable saccharides lactose (Lac) and alginate (Alg). The lactose reduced silver ions to form AgNPs. The crystallite structure of the nanocomposite AgNPs@Lac/Alg, with a mean size of 4-6 nm, was confirmed by analytical techniques. The nanocomposite exhibited high catalytic performance in degrading the pollutants methyl orange and rhodamine B. The antibacterial activity of the nanocomposite is pH-dependent, related to the alterations in surface properties of the nanocomposite at different pH values. At pH 6, the nanocomposite demonstrated the highest antibacterial activity. These findings suggest that this nanocomposite has the potential to be tailored for specific applications in environmental and medicinal treatments, making it a highly promising material.
RESUMEN
In the present study, bioactivity-guided extraction and isolation of the n-hexane fraction of the fruits of Vietnamese Schisandra sphenanthera led to the isolation of five dibenzocyclooctadiene lignans as gomisin N (1), schisandrin C (2), gomisin H (3), gomisin D (4), and gomisin C (5). All the isolates were tested for their inhibition of NO production in LPS-induced RAW 264.7 cells. Among them, compounds 4 and 5 showed weak inhibition of NO production with IC 50 values of 25.0 ± 1.6 and 24.8 ± 2.0 µM, respectively. Compound 1 exhibited NO production inhibition with an IC 50 value of 15.8 ± 2.1 µM, meanwhile, schisandrin C (2) showed the most potent inhibition with an IC 50 value of 8.5 ± 0.5 µM. In addition, compound 2 had a concentrationdependent inhibitory effect on the protein expression of the inflammatory enzymes iNOS and COX-2. Their physicochemical properties and ADMET data were predicted by in silico, indicating favorable drug-like properties as well as low acute oral toxicity. The results suggest that the fruit of S. sphenanthera and its phytochemical constituents might be used as anti-inflammatory agents.