Mercury ions (Hg²⁺) are highly toxic environmental pollutants requiring rapid and cost-effective detection methods. This study presents an eco-friendly approach for the green synthesis of silver nanoparticles (AgNPs) using aqueous extracts of nutmeg (Myristica fragrans), pineapple (Ananas comosus), and jambulang (Syzygium cumini), which serve as both reducing and stabilizing agents. The formation of AgNPs was confirmed by UV-Vis spectroscopy, exhibiting characteristic surface plasmon resonance (SPR) peaks around 400 nm, and TEM analysis, which revealed spherical morphology with diameters of 4–6 nm. The synthesized AgNPs were evaluated as colorimetric sensors for Hg²⁺ detection, based on the color change from yellow/brown to colorless due to the Ag-Hg amalgamation process and the subsequent quenching of the SPR peak. Among the three extracts, S. cumini demonstrated superior performance with a limit of detection (LOD) of 0.47 ppm, significantly lower than A. comosus (1.16 ppm) and M. fragrans (2.51 ppm). To demonstrate practical utility, the AgNPs were immobilized onto cotton buds as a portable, “naked-eye” sensor, enabling on-site mercury detection without sophisticated instrumentation. These results suggest that green-synthesized AgNPs offer a sustainable and efficient platform for monitoring heavy metal contamination in aqueous environments.
Radiation-induced oral mucositis requires predictive biomarkers for personalized therapy. We evaluated salivary IL-6 for predicting severe mucositis and response to food-grade bee products. Secondary analysis of 51 head/neck cancer patients randomized to honey (n = 15), Taiwanese green propolis (TGP, n = 17), or usual-care (n = 19). Both interventions used standardized preparations (10 g in 20 mL water, 10 mL TID). Salivary cytokines were analyzed using ELISA at baseline and during early radiotherapy. Salivary IL-6 at week 3 of radiotherapy demonstrated superior predictive performance (AUC = 0.780, 95
This study proposes an automatic learning activity classification framework based on the Tri Pramana concept using immersive Virtual Reality (VR) video data. Tri Pramana comprising, Sabda, Pratyaksa, and Anumana, is operationalized in this research as a set of observable learning activity categories rather than as a comprehensive epistemological model. Video data were collected from a Fiber Optic Splicing practicum conducted in an immersive VR environment, with each video clip segmented into 5-second sequences. A total of 1,036 video clips were used, consisting of 824 training samples (80
This study addresses the need to prepare students for a technology-driven future by developing and testing the CAFE-AI model, an e-learning framework that integrates pedagogy with artificial intelligence (AI). The model operationalizes four components Content, Activity, Facilitation, and Evaluation (CAFE) through three AI paradigms: AI-directed, AI-supported, and AI-empowered. A research and development (R&D) approach was employed, including design, validation, and empirical testing in a university course. Validation involved subject matter and media experts, while effectiveness was examined using AI literacy and growth mindset instruments. Data were analyzed using descriptive statistics and MANOVA. Results from expert evaluation and a pilot study (n = 80) indicated that the model is highly valid and feasible. A quasi-experimental study (n = 152) demonstrated that CAFE-AI is significantly more effective than conventional e-learning in improving AI literacy and growth mindset (p < 0.05). These findings suggest that the CAFE-AI model provides an adaptive and effective framework for fostering essential digital competencies and promoting a growth-oriented mindset in higher education.
This study aimed to investigate whether Mindful Learning (MiL), Meaningful Learning (MeL), and Joyful Learning (JL) are predictors of Deep Learning (DL) in AI-based language learning. The study employed a convergent mixed-methods design by combining phenomenology and ex-post facto approaches. This study recruited eight EFL students for the interviews and 276 EFL students for the quantitative part. The data were collected through semi-structured interviews, researcher field notes, and questionnaires. The data were analyzed using inductive thematic analysis, descriptive statistics, Pearson Product Moment, and SEM. The findings revealed that qualitatively, MiL, MeL, and JL are related to DL. However, the SEM outputs indicated that JL is the only non-predictor. The study offers four implications directed for the use of AI-based applications in facilitating DL in AI-based learning in EFL contexts.
