Nutritional health of the Rohingya refugees in Bangladesh: Conceptualizing a multilevel action framework focusing the COVID-19.

The Rohingya refugees are among the most vulnerable victims of COVID-19 pandemic in Bangladesh. In refugee camps, they frequently lack access to safe and nutritious foods, drinking water, and a healthy environment. Despite the fact that numerous national and international organizations are sincerely collaborating to meet their nutritional and medical needs, the pace of work has slowed due to COVID-19. Combating COVID-19 demands a robust immune system, which relies heavily on a nutritious diet. The development of strong immunity to protect Rohingya refugees, particularly children and women, through the provision of nutrient-dense foods is thus highly necessary. Consequently, the current commentary focused on the nutritional health status of Rohingya refugees in Bangladesh during COVID-19. In addition, we provided a multilevel implementation framework that could assist stakeholders and policymakers in taking effective measures to recover their nutritional health.

Detection of mold on the food surface using YOLOv5.

The study aimed to identify different molds that grow on various food surfaces. As a result, we conducted a case study for the detection of mold on food surfaces based on the "you only look once (YOLO) v5" principle. In this context, a dataset of 2050 food images with mold growing on their surfaces was created. Images were obtained from our own laboratory (850 images) as well as from the internet (1200 images). The dataset was trained using the pre-trained YOLOv5 algorithm. A laboratory test was also performed to confirm that the grown organisms were mold. In comparison to YOLOv3 and YOLOv4, this current YOLOv5 model had better precision, recall, and average precision (AP), which were 98.10%, 100%, and 99.60%, respectively. The YOLOv5 algorithm was used for the first time in this study to detect mold on food surfaces. In conclusion, the proposed model successfully recognizes any kind of mold present on the food surface. Using YOLOv5, we are currently conducting research to identify the specific species of the detected mold.