Research and Development Prospects for Sugarcane Industry in Vietnam.

Sugarcane is one of the most important industrial crops in Vietnam and covers a total of 127,000 hectares of plantation area. In the season 2020-2021, Vietnam has produced 0.763 million tons of sugar (accounting for 0.34% total world sugar production). A current sugarcane production of 7.498 million tons is being used mainly for sugar production for direct consumption, ethanol production, bio-electricity and fertilization. To ensure crop sustainability, various policies and plans have been implemented. Crop breeding and zoning improvement programme significantly influence sugarcane production and sugar yield. Over 25 years since the programme "one million ton of sugar" was promoted, Vietnam currently possesses 25 sugar mills with a total capacity of 110,000 tons of sugarcane per day. Major problems of sugarcane industry as well as research and development have been discussed in this review. Recent research and development work focused on the added values of co-products to ensure sustainability of the sugarcane industry. Molasses will be used for ethanol production, and bagasse is used as the biomass for the alternative energy. Sugarcane and sugar would be the main feedstocks for those bio-economy growths in Vietnam. To keep the sustainable development of the sugar industry, and to meet the demand of the food and non-food requirements, it is necessary to upgrade the sugar value chain through the adoption and the development of co-products of the sugar industry.

An Efficient CNN-Based Method for Intracranial Hemorrhage Segmentation from Computerized Tomography Imaging.

Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.