Predicting doped thermoelectric properties with multitask attention

Thermoelectric (TE) materials, capable of converting temperature gradients into electricity, and vice versa, emerge as a promising class of sustainable materials because of their pollution-free operation. However, their efficiency remains lower than that of conventional heat engines and pumps, limiting their applications. Thus, much research is geared towards discovering high performing TE materials. One strategy pertains to impurity doping, which have the potential to drastically augment material property despite small amounts of elements added. Experimentally, it is not feasible to synthesize every possible doped material due to the large chemical space involved, necessitating an alternative procedure. Recently, machine learning (ML) has emerged as a powerful tool to accelerate property prediction and the discovery of new materials. Yet, it is challenging to predict doped material properties solely from composition. Typical ML featurization techniques rely on stoichiometric prevalence, resulting in doped materials having similar vectors to their pure forms. Consequently, typical ML models struggle to predict the complex, nonlinear effects of dopants. This work addresses these limitations by enhancing the predictive accuracy of 7 key TE transport property prediction, via the modification of the Compositionally restricted attention-based Network (CrabNet). CrabNet predicts properties of compositions using the attention mechanism, which can be leveraged to learn dopant-host interactions implicitly. First, a comprehensive experimental TE dataset is collated from recent literature, providing a source of high-fidelity data. Second, by utilizing multitask learning to exploit the interdependence of different TE transport properties, and encoding temperature information, the modified CrabNet model demonstrates improved prediction accuracy over conventional and existing ML models geared towards TE property prediction.Bachelor's degre

A comparative analysis of metaphorical cognition in ChatGPT and human minds

ChatGPT represents a significant advancement in the field of Artificial Intelligence (AI), showcasing the development of a robust AI system capable of multitasking and generating human-like language. At present, many scholars have done evaluations on ChatGPT in terms of language, reasoning, and scientific knowledge abilities, based on benchmarks or well-crafted questions. However, to the best of our knowledge, there is currently no existing comparative analysis from a cognitive perspective that directly assesses ChatGPT alongside humans. Metaphor, serving as a manifestation of linguistic creativity, provides a valuable avenue for examining cognition. This is due to the mapping relationship it establishes between the target and source conceptual domains, reflecting distinct cognitive patterns. In this paper, we use a metaphor processing tool, MetaPro, to analyze the cognitive differences between ChatGPT and humans through the metaphorical expressions in ChatGPT- and human-generated text. We illustrate the preferences in metaphor usage, concept mapping, and cognitive pattern variances across different domains. The methodology utilized in this study makes a valuable contribution to the task-agnostic evaluation of AI systems and cognitive research. The insights garnered from this research prove instrumental in comprehending the cognitive distinctions between ChatGPT and humans, facilitating the identification of potential cognitive biases within ChatGPT.Ministry of Education (MOE)This research/project is supported by the Ministry of Education, Singapore, under its MOE Academic Research Fund Tier 2 (STEM RIE2025 Award MOE-T2EP20123-0005). Guanyi Chen is supported by the Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning and the National Language Resources Monitoring and Research Center for Network Media of Central China Normal University in Wuhan, China

Radio-frequency (RF) sensing for deep awareness of human physical status

This project explores the use of WiFi-based Radio-Frequency (RF) sensing for human activity recognition (HAR), enabling deep awareness of human physical status. Traditional HAR methods rely on wearable sensors or vision-based systems, which can be intrusive, impractical, or constrained to idealized environments. This study leverages Channel State Information (CSI) extracted from WiFi signals to recognize human activities involving multiple individuals using standard household WiFi routers and smartphones. A comprehensive dataset was collected using commercially available WiFi devices, capturing various activities in dynamic conditions. Several deep learning models were implemented and compared, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Attention-Based GRU (ABGRU), CNN-GRU, Siamese GRU (SGRU), and Transformer-based networks. The results demonstrate that the top-performing models achieve over 92\% accuracy in activity classification. This research underscores the feasibility of WiFi-based HAR for applications such as smart homes, elderly monitoring, and security systems. Future work will focus on expanding the dataset, improving model robustness in noisy environments, and enhancing real-world deployment feasibility.Bachelor's degre

Integration of large language models in game development

This final year project seeks to create a game titled The Last Unwritten that seeks to explores the integration of Large Language Models (LLMs) into game design. The game architecture combines a Unity-based front end with a Flask API server that interfaces with local LLMs deployed using Ollama. LLM features explored by the project include question generation, narrative pickups, and dynamic NPC dialogue. The report includes the design and implementation of The Last Unwritten and the testing of systems for reliability. It also compares the performance of the original and LLM-enhanced versions by surveying players about their experience. The report concludes with an evaluation and future work.Bachelor's degre

Biophysical approach for precision membrane engineering of lipid nanoparticle systems

Multivalent ligand-receptor interactions involving soft-matter, membrane-enveloped biological and biomimetic nanoparticles (e.g., virus particles, exosomes, vesicles, lipid nanoparticles) at cellular membrane interfaces are critical to a wide range of biological functions and pathologies. This class of multivalent interactions has been widely explored by biological and biophysical measurement approaches but it has proven analytically challenging to track corresponding multivalency-related nanoparticle shape deformation processes due to limitations in conventional measurement approaches. Such deformation processes are biologically important and influenced by a balance between the multivalent binding interaction energy and membrane bending energy of soft-matter nanoparticles. From an engineering perspective, developing measurement approaches to characterize multivalency-induced nanoparticle shape deformation at lipid membrane interfaces can help to provide a biophysical understanding about how various design parameters affect the membrane nanomechanical properties of nanoparticles. As a model experimental system to characterize soft-matter nanoparticle-membrane interactions, this thesis presents a localized surface plasmon resonance (LSPR)-based measurement platform to characterize the binding interaction of ligand-modified lipid vesicles with a receptor-functionalized supported lipid bilayer (SLB) platform and to obtain nanomechanical insights into multivalency-induced vesicle shape deformation processes based on a combination of experiment and theory. Within this scope, analytical models of the LSPR-related physics and multivalent ligand-receptor complex dynamics were developed in order to quantify structural and energetic aspects of vesicle deformation. By utilizing this measurement approach and corresponding analytical models, it was possible to elucidate how key parameters like receptor and ligand densities, vesicle size, cholesterol fraction in vesicles, and solution pH affect multivalency-induced vesicle attachment and shape deformation processes. This study discusses the results in terms of the analytical merits of the LSPR technique to track nanoparticle shape deformation at lipid membrane interfaces compared to other measurement options as well as insights into how multivalent interactions affect the nanomechanical properties of lipid vesicles. The measurement approach and analytical framework developed in this thesis can be broadly extended to evaluate the multivalent interactions of different types of membrane-enveloped biological and biomimetic nanoparticles with engineered properties and also to demonstrate the utility of membrane biophysics approaches to study interfacial phenomena related to nanoparticle-membrane interactions in general.Doctor of Philosoph

Is the Malaysia-China “two countries, twin parks” project meeting expectations?

The Malaysia-China twin industrial parks project in Kuantan and Qinzhou has made economic contributions to both countries. They are due for a thorough review to enhance their bilateral relevance for these shifting geoeconomic times.Published versio

AI based chatbot

AssistAI is an AI-powered chatbot designed to address the academic and research needs of faculty members in higher education. Leveraging state-of-the-art Large Language Models (LLMs), AssistAI simplifies complex academic workflows by automating research assistance, lecture note generation, and assessment creation. The chatbot provides tailored solutions such as automated paper discovery, content summaries, structured presentation outlines, and quiz development with customizable difficulty levels. By integrating features like conversational interfaces, proactive task management, and adaptive functionality, AssistAI enhances productivity while minimizing the administrative burden on educators.Bachelor's degre

Socio-economic and biophysical drivers of protected area growth in Asia Pacific

The world is facing a biodiversity crisis and protected areas (PA) is one of the most effective ways of conservation. Asia Pacific (APAC) being a rapidly developing region on earth, sees land use changes happening at unprecedented rates, leading to further environmental degradation. Yet, little to known about the socio-economic and biophysical indicators driving PA expansion in the APAC region. As such, this work aims to investigate the socio-economic and biophysical indicators which might influence the extent of PA expansion within a country. Generalized linear models (GLM) were used to investigate the relationships of the socio- economic and biophysical indicators for PA expansion at a country level. The results from the GLMs suggest that economic growth initially decreases the extent of PA expansion but eventually promotes greater conservation, aligning with the Environmental Kuznets Curve hypothesis. Over time, as countries in the APAC region advance economically, the primary drivers of PA expansion shift from socio-economic to predominantly biophysical factors. Given that economic development is crucial for achieving higher levels of terrestrial land protection, less-developed countries aiming to meet the 30% conservation target by 2030 under the Global Biodiversity Framework will need to "leapfrog" traditional development stages.Bachelor's degre

Non-asymptotic convergence bounds for modified tamed unadjusted Langevin algorithm in non-convex setting

We consider the problem of sampling from a high-dimensional target distribution πβ on Rd with density proportional to θ↦e−βU(θ) using explicit numerical schemes based on discretising the Langevin stochastic differential equation (SDE). In recent literature, taming has been proposed and studied as a method for ensuring stability of Langevin-based numerical schemes in the case of super-linearly growing drift coefficients for the Langevin SDE. In particular, the Tamed Unadjusted Langevin Algorithm (TULA) was proposed in [2] to sample from such target distributions with the gradient of the potential U being super-linearly growing. However, theoretical guarantees in Wasserstein distances for Langevin-based algorithms have traditionally been derived assuming strong convexity of the potential U. In this paper, we propose a novel taming factor and derive, under a setting with possibly non-convex potential U and super-linearly growing gradient of U, non-asymptotic theoretical bounds in Wasserstein-1 and Wasserstein-2 distances between the law of our algorithm, which we name the modified Tamed Unadjusted Langevin Algorithm (mTULA), and the target distribution πβ. We obtain respective rates of convergence O(λ) and O(λ1/2) in Wasserstein-1 and Wasserstein-2 distances for the discretisation error of mTULA in step size λ. High-dimensional numerical simulations which support our theoretical findings are presented to showcase the applicability of our algorithm.Ministry of Education (MOE)Financial support by the Ministry of Education, Singapore, under its MOE AcRF Tier 2 Grant MOE-T2EP20222-0013, and by the Guangzhou-HKUST(GZ) Joint Funding Program (No. 2024A03J0630) is gratefully acknowledged

Radio-frequency (RF) sensing for deep awareness of human physical status

Over the last few years, there have been a variety of human sensing applications developed through Radiofrequency (RF) sensing engaged in multiple different sectors. Traditional human activity recognition (HAR) methods have involved the use of sensors, which can be inconvenient and invade the user’s privacy. As such, Wi-Fi sensing, a type of RF sensing, provides a contactless yet effective way to achieve similar effects as traditional sensors. In this project, a Bidirection Long Short-Term Memory (BiLSTM) model was used to train channel state information (CSI) data from Wi-Fi signals collected in an indoor environment, which achieved an accuracy of 83.65% when classifying between static and dynamic actions. Alongside the use of the wavelet denoising method, the results indicate that while human activities can be classified with CSI information with high accuracy, further research is still necessary for improved accuracy and applicability of the model to the real-world environment.Bachelor's degre