Recent Advances in Carbohydrate Chemistry and Application

Recent advances in carbohydrate chemistry and glycobiology have led to a better understanding of carbohydrates and the associated biological glycosylation in biology. This report describes new advances in carbohydrate synthesis and its application to the understanding of glycosylation in protein folding, cancer progression, influenza and SARS-CoV-2 infection and the development of carbohydrate-based medicines. © The AIC 2023. All rights reserved.

An Optimized Deep Learning Model for Automatic Diagnosis of COVID-19 Using Chest X-Ray Images

COVID-19 has caused havoc throughout the world in the last two years by infecting over 455 million people. Development of automatic diagnosis software tools for rapid screening of COVID-19 via clinical imaging such as X-ray is vital to combat this pandemic. An optimized deep learning model is designed in this paper to perform automatic diagnosis on the chest X-ray (CXR) images of patients and classify them into normal, pneumonia and COVID-19 cases. A convolutional neural network (CNN) is employed in optimized deep learning model given its excellent performances in feature extraction and classification. A particle swarm optimization with multiple chaotic initialization scheme (PSOMCIS) is also designed to fine tune the hyperparameters of CNN, ensuring the proper training of network. The proposed deep learning model, namely PSOMCIS-CNN, is evaluated using a public database consists of the CXR images with normal, pneumonia and COVID-19 cases. The proposed PSOMCIS-CNN is revealed to have promising performances for automatic diagnosis of COVID-19 cases by producing the accuracy, sensitivity, specificity, precision and F1 score values of 97.78%, 97.77%, 98.8%, 97.77% and 97.77%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Performance Comparison of Convolutional Neural Network for COVID-19 Diagnosis

COVID-19 has devastated the global healthcare system as well as the world economy with more than 600 million confirmed cases and 6 million deaths globally. A timely and accurate diagnosis of the disease plays a vital role in the treatment and preventative spread of disease. Recently, deep learning such as Convolutional Neural Networks (CNNs) have achieved extraordinary results in many applications such as medical classifications. This work focuses on investigating the performance of nine state-of-the-art architectures: Alexnet, Googlenet, Inception-v3, Mobilenet-v2, Resnet-18, Resnet-50, Shufflenet, Squeezenet and Resnet-50 RCNN for COVID-19 classification by comparing with performance metrics such as accuracy, precision, sensitivity, specificity and F-score. The datasets considered in current study are divided into three different classes namely Normal Chest X-Rays (CXRs), Pneumonia patient CXR and COVID-19 patient CXR. The results achieved shows that Resnet-50 RCNN achieved an accuracy, precision, sensitivity, specificity and F-score of 95.67%, 95.71%, 95.67%, 97.84% and 95.67% respectively. © The 2023 International Conference on Artificial Life and Robotics (ICAROB2023).

Using Augmented Reality as a Powerful and Innovative Technology to Increase Enthusiasm and Enhance Student Learning in Higher Education Chemistry Courses

The promotion of spatial skills is essential in chemistry education. However, the process of acquiring these skills can be monotonous if learning is limited to the memorization of Newman projections or 3D molecular kits. Existing approaches to learning using visualizing tools require physical models which limit learning activities to within the classroom. Augmented reality (AR) in chemistry education allows students to see actual compound representation in a 3D environment, inspect compounds from multiple viewpoints, and control compounds interaction in real-Time in any location. This facilitates the understanding of the spatial relations between compounds. We developed a methodology to use and assess an AR program to teach chemistry to associate degree science students. Figures of small organic molecules together with customized AR cards were used to let students appreciate the complexity of a 3D compound structure by viewing and rotating the depicted compounds. The effectiveness of learning chemistry using AR technology was evaluated. Quantitative questionnaire feedback results from students showed that 87% found that using AR technology for chemistry subjects was an effective teaching method that enhanced their learning, and students were satisfied with the AR educational app and the AR materials used. In a pre-and post-Test evaluation of a group activity, students learned better and remembered more information about functional groups and drawings of complicated compounds after using AR technology. On the basis of our results, we can conclude that using AR has a positive impact on enthusiasm and learning in higher education chemistry courses for subdegree students, and this technology should be broadly used as a digital tool to promote active learning during the COVID-19 pandemic. ©