Application of Generative Artificial Intelligence Models for Accurate Prescription Label Identification and Information Retrieval for the Elderly in Northern East of Thailand.

This study introduces a novel AI-driven approach to support elderly patients in Thailand with medication management, focusing on accurate drug label interpretation. Two model architectures were explored: a Two-Stage Optical Character Recognition (OCR) and Large Language Model (LLM) pipeline combining EasyOCR with Qwen2-72b-instruct and a Uni-Stage Visual Question Answering (VQA) model using Qwen2-72b-VL. Both models operated in a zero-shot capacity, utilizing Retrieval-Augmented Generation (RAG) with DrugBank references to ensure contextual relevance and accuracy.

Cleft Lip and Palate Classification Through Vision Transformers and Siamese Neural Networks.

This study introduces a novel approach for the diagnosis of Cleft Lip and/or Palate (CL/P) by integrating Vision Transformers (ViTs) and Siamese Neural Networks. Our study is the first to employ this integration specifically for CL/P classification, leveraging the strengths of both models to handle complex, multimodal data and few-shot learning scenarios. Unlike previous studies that rely on single-modality data or traditional machine learning models, we uniquely fuse anatomical data from ultrasound images with functional data from speech spectrograms.

Enhancing the service quality of community pharmacies for non-prescription medicines based on the perspectives of pharmacists and patients: a qualitative study.

Background: Pharmacies in several countries are encountering a fierce competition from both local and international rivals, resulting in significant challenges in maintaining competitiveness and market share. Pharmacies are important locations for those seeking non-prescription medicines and are equipped to manage a wide variety of ailments. However, despite the large number of previous qualitative studies on the service quality of community pharmacies, very few studies have explored the service quality dimensions, specifically for non-prescription medications.

Reuse of thermosetting plastic waste for lightweight concrete.

This paper presents the utilization of thermosetting plastic as an admixture in the mix proportion of lightweight concrete. Since this type of plastic cannot be melted in the recycling process, its waste is expected to be more valuable by using as an admixture for the production of non-structural lightweight concrete. Experimental tests for the variation of mix proportion were carried out to determine the suitable proportion to achieve the required properties of lightweight concrete, which are: low dry density and acceptable compressive strength.