Kevin Wong

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) serves as a key tool for the test of lipophilic substances in laboratory medicine and is widely employed in the analysis of coenzyme Q10 (CoQ10) and 25-hydroxyvitamin D (25OHD). In this paper, fuzzy concept was applied to improve the LC-MS/MS methods used for CoQ10 and 25OHD detection. The focus was placed on selecting the optimal mobile phase for CoQ10 analysis and examining the differences between LC-MS/MS and chemiluminescence immunoassay (CLIA) methods for 25(OH)D measurement. Through screening various organic phase combinations and employing fuzzy inference, the optimal mobile phase ratio for CoQ10 test is determined to be methanol and isopropanol at a ratio of 8:2. Additionally, fuzzy logic was employed to analyze the variations in 25OHD concentrations across different sexes and age groups. The results showed that women aged 30–40 exhibited greater differences in 25(OH)D levels compared to other groups. This study shows that the use of fuzzy concepts can enhance the adaptability and accuracy of LC-MS/MS detection, offering a novel approach to the analysis of lipophilic substances.

Vaccine hesitancy is still a significant barrier to achieving widespread immunity in many communities. In this paper, we evaluated a serious game fo-cusing on vaccination against COVID-19. This study investigates the potential of virtual reality (VR) as an innovative educational tool to address this issue. Focusing on the serious game " Spike Force " , which simulates the mechanisms of the mRNA COVID-19 vaccine, this research evaluates the game's effectiveness in enhancing participants' understanding, altering attitudes, and influencing behaviours related to vaccination. Participants engaged with " Spike Force, " and their knowledge, attitudes, and behaviours were assessed through pre-and post-gameplay questionnaires. The findings show that immersive VR experiences can significantly improve vaccine literacy, increase confidence in vaccine-related discussions, and promote positive behavioural changes toward vaccination. These results suggest that VR could play an effective advocacy role for public health education, particularly in combating vaccine hesitancy.

—Question answering over knowledge graphs (KGQA) seeks to automatically answer natural language questions by retrieving triples within the knowledge graph (KG). In the context of multi-hop KGQA, reasoning across multiple edges of the KG becomes crucial for obtaining answers. Existing methods align with either the path-searching-based mainstream, emphasizing structural KG analysis, or the subgraph-based mainstream, focusing on semantic KG embeddings. Both streams have two primary challenges: (1) KG incompleteness, where path searching or subgraph construction faces limitations in the absence of links between entities; (2) candidate answer selection, wherein most approaches employ pre-defined searching sizes or heuristics. Many recent studies incorporate Graph Convolutional Network (GCN) to encode KGs, yet they overlook the potential over-smoothing issue inherent in GCNs. The over-smoothing problem arises from the tendency of closely connected nodes to exhibit similar embeddings within the deep convolutional architecture of GCNs. To address these challenges, this paper proposes a two-stage framework named ComPath, leveraging insights from both mainstreams. ComPath utilizes GCN to tackle KG incompleteness and introduces a path analyser to mitigate the over-smoothing issue associated with GCN. Candidate answers are selected using semantic similarity. The ablation studies and comparative experiments on the three KGQA benchmark datasets shown that the proposed ComPath performed better than the other KGQAs.

Vaccine hesitancy and uptake have been important issues in controlling the current COVID-19 pandemic in many regions around the globe, but the increase in vaccination rates has been slow or even halted in some countries. Therefore, people who have hesitated in getting the vaccine need to be addressed. One driver influencing vaccination uptake is closing the knowledge gap among the public by equipping them with a deeper understanding of how a vaccine works inside our cells to activate the immune system and develop immunity. Viral immunology is highly conceptual and requires an appreciation of molecular biology in the cell. To give individuals an intuitive awareness of the operation of a mRNA-type virus vaccine for COVID-19, we designed and developed a Virtual Reality (VR) based serious game called ‘Cell Traveler’. Through this innovative VR serious game, the player can control and interact with a sequence of critical real-life events inside a cell triggered by the injected mRNA COVID-19 vaccine. In this paper, we describe the prototype of the ‘Cell Traveler’. We utilize the concepts of serious game to create an experience to encourage students and the public to develop deeper mRNA vaccine knowledge through a memorable and fun experience.

The increased penetration of renewable distributed generation (DG) such as wind power and photovoltaics (PV) in distribution networks has brought significant economic and environmental benefits to society. However, due to the intermittent nature of wind and solar, massive integration of such kinds of DG might cause severe problems, such as greater power loss, reduced power quality, and system efficiency. The integration of energy storage systems (ESS) provides an effective way to handle the above issues due to its capability of stabilizing voltage and frequency. Hence, it is necessary to optimally allocate the ESS to exert maximum support on the distribution network. This paper presents an approach for optimal allocation of ESS to improve voltage profile and reduce power losses and line loading in the unbalanced distribution system. The proposed methodology is tested on an unbalanced medium voltage IEEE-33 bus system with higher wind and PV generation. DIgSILENT PowerFactory is used for constructing and testing the system model. The simulation results demonstrate the effectiveness of the proposed approach in improving the power quality and system efficiency of unbalanced distribution systems.

Resilience refers to a person's mental ability to adaptively deal with challenges in life. Video games (both commercial games and serious games) have been used as effective resilience interventions. There is some evidence that commercial puzzle video games could increase resilience as they involve overcoming frustration to succeed. This research explores if single-player commercial puzzle video games can be used as an effective intervention to improve resilience. Participants were adolescents who attended an after-school club for 8-10 weeks. This paper presents the case studies of two club participants and their gameplay experiences. Data was collected through surveys, interviews, gameplay recordings and journals. In both case studies the participants both give and receive guidance and support from others. Having a 'more knowledgeable other' present while playing a challenging game helped participants deal with frustration and persevere. This paper provides a first step towards exploring the relationship between puzzle video game play, resilience and social support from others.

Recently, extensive use of digital music has led to an increase in songs in online music applications and personal music libraries. In large music libraries, songs which are not listened to regularly, most probably will go unnoticed. There are many ways of browsing songs in an online music library. In the field of Music Information Retrieval (MIR), some type of visual forms such as colour, avatar, mood picture and album cover to visualise music, have been introduced. However, there is no research focusing explicitly on textures. To create new method of browsing music, we proposed a framework to visualise music mood using visual texture. In order to determine how well people can interact with the visual texture to browse songs in music library, usability testing was conducted. In this paper, we will present the results of the usability testing.

Existing basic artificial neurons merge multiple weighted inputs and generate a single activated output. This paper explores the applicability of a new structure of a neuron, which merges multiple weighted inputs like existing neurons, but instead of generating single output, it generates multiple outputs. The proposed “Multiple Output Neuron” (MON) can reduce computation in a basic XOR network. Furthermore, a MON based convolutional neural network layer (MONL) is described. Proposed MONL can backpropagate errors, thus can be used along with other CNN layers. MONL reduces the network computations, by reducing the number of filters. Reduced number of filters limits the network performance, thus MON based neuroevolution (MON-EVO) technique is also proposed. MON-EVO evolves the MONs into single output neurons for further improvement in training. Existing neuroevolution techniques do not utilize backpropagation but MONs can utilize backpropagation. Experimental networks trained using the CIFAR-10 classification dataset show that proposed MONL and MON-EVO provide a solution for reduced training computation and neuroevolution using backpropagation.

Follicular Lymphoma (FL) is a type of lymphoma that grows silently and is usually diagnosed in its later stages. To increase the patients' survival rates, FL requires a fast diagnosis. While, traditionally, the diagnosis is performed by visual inspection of Whole Slide Images (WSI), recent advances in deep learning techniques provide an opportunity to automate this process. The main challenge, however, is that WSI images often exhibit large variations across different operating environments, hereinafter referred to as sites. As such, deep learning models usually require retraining using labeled data from each new site. This is, however, not feasible since the labelling process requires pathologists to visually inspect and label each sample. In this paper, we propose a deep learning model that uses transfer learning with fine-tuning to improve the identification of Follicular Lymphoma on images from new sites that are different from those used during training. Our results show that the proposed approach improves the prediction accuracy with 12% to 52% compared to the initial prediction of the model for images from a new site in the target environment.