MC-NN: An End-to-End Multi-Channel Neural Network Approach for Predicting Influenza A Virus Hosts and Antigenic Types

Influenza poses a significant threat to public health, particularly among the elderly, young children, and people with underlying dis-eases. The manifestation of severe conditions, such as pneumonia, highlights the importance of preventing the spread of influenza. An accurate and cost-effective prediction of the host and antigenic sub-types of influenza A viruses is essential to addressing this issue, particularly in resource-constrained regions. In this study, we propose a multi-channel neural network model to predict the host and antigenic subtypes of influenza A viruses from hemagglutinin and neuraminidase protein sequences. Our model was trained on a comprehensive data set of complete protein sequences and evaluated on various test data sets of complete and incomplete sequences. The results demonstrate the potential and practicality of using multi-channel neural networks in predicting the host and antigenic subtypes of influenza A viruses from both full and partial protein sequences.Comment: Accepted version submitted to the SN Computer Science; Published in the SN Computer Science 202

DISPATCHING AND CONFLICT-FREE ROUTING OF VEHICLES IN NEW CONCEPTUAL AUTOMATED CONTAINER TERMINALS

Ph.DDOCTOR OF PHILOSOPH

Oxidation Analyses of Massive Air Ingress Accident of HTR-PM

The double-ended guillotine break (DEGB) of the horizontal coaxial gas duct accident is a serious air ingress accident of the high temperature gas-cooled reactor pebble-bed module (HTR-PM). Because the graphite is widely used as the structure material and the fuel element matrix of HTR-PM, the oxidation analyses of this severe air ingress accident have got enough attention in the safety analyses of the HTR-PM. The DEGB of the horizontal coaxial gas duct accident is calculated by using the TINTE code in this paper. The results show that the maximum local oxidation of the matrix graphite of spherical fuel elements in the core will firstly reach 3.75⁎104 mol/m3 at about 120 h, which means that only the outer 5 mm fuel-free zone of matrix graphite will be oxidized out. Even at 150 h, the maximum local weight loss ratio of the nuclear grade graphite in the bottom reflectors is only 0.26. Besides, there is enough time to carry out some countermeasures to stop the air ingress during several days. Therefore, the nuclear grade graphite of the bottom reflectors will not be fractured in the DEGB of the horizontal coaxial gas duct accident and the integrity of the HTR-PM can be guaranteed

Autoencoder with Group-based Decoder and Multi-task Optimization for Anomalous Sound Detection

In industry, machine anomalous sound detection (ASD) is in great demand. However, collecting enough abnormal samples is difficult due to the high cost, which boosts the rapid development of unsupervised ASD algorithms. Autoencoder (AE) based methods have been widely used for unsupervised ASD, but suffer from problems including 'shortcut', poor anti-noise ability and sub-optimal quality of features. To address these challenges, we propose a new AE-based framework termed AEGM. Specifically, we first insert an auxiliary classifier into AE to enhance ASD in a multi-task learning manner. Then, we design a group-based decoder structure, accompanied by an adaptive loss function, to endow the model with domain-specific knowledge. Results on the DCASE 2021 Task 2 development set show that our methods achieve a relative improvement of 13.11% and 15.20% respectively in average AUC over the official AE and MobileNetV2 across test sets of seven machines.Comment: Submitted to the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024

Leveraging Machine Learning for the Analysis and Prediction of Influenza A Virus

Influenza, commonly known as flu, is a respiratory disease that poses a significant challenge to global public health due to its high prevalence and potential for serious health complications. The disease is caused by influenza viruses, among which influenza A viruses are of particular concern. These viruses are known for their rapid transmission, potential to cause severe health issues, and frequent mutations, which underscore the need for ongoing research and surveillance. A key aspect of managing influenza outbreaks includes understanding host origins, antigenic properties, and the ability of influenza A viruses to transmit between species, as this knowledge is critical in forecasting outbreaks and developing effective vaccines. Traditional approaches, such as hemagglutination inhibition assays for antigenicity assessment and phylogenetic analysis to determine genetic relationships, host origins and subtypes, have been fundamental in understanding influenza viruses. These methods, while informative, often face limitations in terms of time, resources, and the ability to keep pace with the rapid evolutionary changes of viruses. To mitigate these limitations, this thesis uses advanced machine learning techniques to analyse critical protein sequence data from influenza A viruses, offering an alternative perspective for unravelling the complexities of influenza, and potentially opening new avenues for analysis without strict reliance on prior biological knowledge. The core of the thesis is the application and refinement of predictive models to determine host origins, subtypes, and antigenic relationships of influenza A viruses. These models are evaluated comprehensively, considering factors such as the impact of incomplete sequences, performance across various host taxonomies and individual hosts, as well as the influence of reference databases on model performance. This evaluation illuminates the potential of machine learning to enhance our understanding of influenza A viruses in real-world scenarios, pointing out the ongoing importance of this research in public health