Sentiment analysis of preservice teachers' reflections using a large language model

In this study, the emotion and tone of preservice teachers' reflections were analyzed using sentiment analysis with LLMs: GPT-4, Gemini, and BERT. We compared the results to understand how each tool categorizes and describes individual reflections and multiple reflections as a whole. This study aims to explore ways to bridge the gaps between qualitative, quantitative, and computational analyses of reflective practices in teacher education. This study finds that to effectively integrate LLM analysis into teacher education, developing an analysis method and result format that are both comprehensive and relevant for preservice teachers and teacher educators is crucial.

An automated, high-resolution phenotypic assay for adult Brugia malayi and microfilaria

Brugia malayi are thread-like parasitic worms and one of the etiological agents of Lymphatic filariasis (LF). Existing anthelmintic drugs to treat LF are effective in reducing the larval microfilaria (mf) counts in human bloodstream but are less effective on adult parasites. To test potential drug candidates, we report a multi-parameter phenotypic assay based on tracking the motility of adult B. malayi and mf in vitro. For adult B. malayi, motility is characterized by the centroid velocity, path curvature, angular velocity, eccentricity, extent, and Euler Number. These parameters are evaluated in experiments with three anthelmintic drugs. For B. malayi mf, motility is extracted from the evolving body skeleton to yield positional data and bending angles at 74 key point. We achieved high-fidelity tracking of complex worm postures (self-occlusions, omega turns, body bending, and reversals) while providing a visual representation of pose estimates and behavioral attributes in both space and time scales.

Parameter extraction of Extended Floating Gate Field Effect Transistors (EGFETs): Estimating the threshold voltage, series resistance, and mobility degradation from I-V measurements

Extended Floating Gate Field Effect Transistors (EGFETs) are CMOS-compatible floating gate devices capable of detecting charges on their sensing area by the relative shifts in current-voltage (I-V) characteristics. The I-V shifts are generally computed by measuring the EGFET parameters in the strong inversion region of operation. This could lead to errors in estimating the device sensitivity because the simple I-V model ignores the mobility degradation and series resistance effects in EGFETs. Our goal is to model these parasitic effects and present methods to extract the key device parameters. We derive an analytical I-V model for EGFETs in the linear region of transistor operation, accounting for both the mobility degradation and series resistance effects. Based on the analytical model, three methods are presented to estimate the key parameters, namely the threshold voltage, series resistance, surface roughness parameter, low-field mobility, and effective mobility from the I-V characteristics, gate transconductance, and drain conductance. The peak transconductance method is used as a benchmark for comparing the extracted threshold voltages. Silicon-based EGFET devices are fabricated, and their I-V characteristics are measured with deionized water and three polyelectrolytes. From the I-V data, the parameter extraction methods are used to compute the values of the key parameters, and the suitability of each method is discussed. The gate transconductance methods show good agreement between the values for the key parameters, while the drain transconductance method gives lower values of the key parameters. There is scope to improve the presented methods by incorporating the effects of substrate bias and asymmetric series resistance for new extended-gate device architectures, including solution-based organic field-effect transistors.