RECIPE: How to Integrate ChatGPT into EFL Writing Education

The integration of generative AI in the field of education is actively being explored. In particular, ChatGPT has garnered significant interest, offering an opportunity to examine its effectiveness in English as a foreign language (EFL) education. To address this need, we present a novel learning platform called RECIPE (Revising an Essay with ChatGPT on an Interactive Platform for EFL learners). Our platform features two types of prompts that facilitate conversations between ChatGPT and students: (1) a hidden prompt for ChatGPT to take an EFL teacher role and (2) an open prompt for students to initiate a dialogue with a self-written summary of what they have learned. We deployed this platform for 213 undergraduate and graduate students enrolled in EFL writing courses and seven instructors. For this study, we collect students' interaction data from RECIPE, including students' perceptions and usage of the platform, and user scenarios are examined with the data. We also conduct a focus group interview with six students and an individual interview with one EFL instructor to explore design opportunities for leveraging generative AI models in the field of EFL education

Abstract Short communication Multi-user data multiplexing for digital multimedia broadcasting

following a period of time of careful reflection. Now, the next generation of digital broadcast services will not be limited to digital audio but also extended to a variety of multimedia data services as well as mobile application services. However, the current approaches for transporting multimedia content over wireless networks for the DMB require significant overhead. In this paper, we propose a new approach for multi-user data multiplexing with several access points serving a number of wireless terminals for the DMB. By modifying the multiplexer header, multiplexing time, and handling of time-critical data, the duplication overhead can be considerably decreased when compared to existing schemes. Ó 2006 Elsevier B.V. All rights reserved

Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response

Advancing fluorescence imaging: enhanced control of cyanine dye-doped silica nanoparticles

Abstract Background Silica nanoparticles (SNPs) have immense potential in biomedical research, particularly in drug delivery and imaging applications, owing to their stability and minimal interactions with biological entities such as tissues or cells. Results With synthesized and characterized cyanine-dye-doped fluorescent SNPs (CSNPs) using cyanine 3.5, 5.5, and 7 (Cy3.5, Cy5.5, and Cy7). Through systematic analysis, we discerned variations in the surface charge and fluorescence properties of the nanoparticles contingent on the encapsulated dye-(3-aminopropyl)triethoxysilane conjugate, while their size and shape remained constant. The fluorescence emission spectra exhibited a redshift correlated with increasing dye concentration, which was attributed to cascade energy transfer and self-quenching effects. Additionally, the fluorescence signal intensity showed a linear relationship with the particle concentration, particularly at lower dye equivalents, indicating a robust performance suitable for imaging applications. In vitro assessments revealed negligible cytotoxicity and efficient cellular uptake of the nanoparticles, enabling long-term tracking and imaging. Validation through in vivo imaging in mice underscored the versatility and efficacy of CSNPs, showing single-switching imaging capabilities and linear signal enhancement within subcutaneous tissue environment. Conclusions This study provides valuable insights for designing fluorescence imaging and optimizing nanoparticle-based applications in biomedical research, with potential implications for targeted drug delivery and in vivo imaging of tissue structures and organs. Graphical Abstrac

Influence of Intercell Trapped Charge on Vertical NAND Flash Memory

The influence of intercell trapped charge (ITC)-the charge trapped at the inter-cell nitride regions by fringe electric fields during programand erase operations-on vertical NAND (VNAND) flash memory is investigated. In addition to conventional degradation mechanisms such as tunnel oxide damage, ITC deteriorates the transconductance and read current of VNAND flash memory cells. The influence of ITC-induced degradationon VNAND flashmemory is discussed, using both simulation and experimental results. A solution for ITC suppression is also proposed: the use of low-k intercell regions.N