http://hdl.handle.net/10453/19979 2026-04-24T02:43:12Z http://hdl.handle.net/10453/194792 Title: Application of Digital Transformation Techniques for Inbound, Inplant and Outbound Transport Logistics Authors: Bajpai, Priyam Abstract: This thesis addresses key challenges in Transport Logistics (TL) types—inbound, inplant, outbound, and reverse—through digital transformation (DT) techniques like optimization, AI, and Digital Twins. It develops a TL framework, proposes novel models, and offers insights for improving efficiency, sustainability, and disruption mitigation. Three focused studies demonstrate practical strategies for professionals and highlight future research directions in circular economy and reverse logistics. Description: University of Technology Sydney. Faculty of Business. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194771 Title: Laser refrigeration of high-doped Yb3+ nanocrystals Authors: Lu, Ming Abstract: Ytterbium-doped materials are promising for laser cooling because Yb³⁺ ions combine favorable optical properties with a simple two-level structure that minimizes nonradiative losses and enhances cooling via anti-Stokes fluorescence. It allows for efficient extraction of thermal energy from the material, enabling localized temperature reduction. Despite these advantages, achieving efficient cooling in nanoscale remains a significant challenge. One of the primary obstacles lies in overcoming the concentration quenching effect at high doping levels. While high doping concentrations are essential for increasing absorption and quantum efficiency thus achieving stronger cooling, they also lead to enhanced nonradiative energy transfer between neighboring Yb³⁺ ions, resulting in a reduction of cooling efficiency. Therefore, developing strategies to mitigate concentration quenching while maintaining high absorption and quantum efficiency is crucial for advancing Yb³⁺ based laser cooling systems. Hence, in this thesis, we explore the integration of optical microcavities to mitigate concentration quenching while maintaining high absorption efficiency and enlarging the anti-Stokes energy gap. This approach leverages the high Q-factor and enhanced light-matter interactions of microcavities to enable effective laser cooling at high Yb³⁺ doping levels. Furthermore, the study demonstrates the application of laser cooling in super-resolved force microscopy, where reduced thermal noise enhances force sensitivity. Description: University of Technology Sydney. Faculty of Science. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194770 Title: Efficient Learning-based Trajectory Generation for Predicting Future Road Network Authors: Chen, Yunting Abstract: Trajectory generation is a key application in spatio-temporal data mining, especially in the era of generative artificial intelligence. By leveraging generative models, it becomes possible to extract temporal–spatial patterns from historical trajectory data and predict future road network behaviors. Trajectory simulation now plays an essential role in navigation, traffic optimization, and mobility analysis. From a modeling standpoint, trajectory data can be viewed as multidimensional time series integrating temporal dynamics and spatial coordinates. Through reviewing existing time series generation frameworks, we identified their limitations and developed improved approaches to advance trajectory synthesis. Chapter 1 introduces the motivation and background of deep learning–based trajectory generation. Although existing time series generation and prediction models offer practical value, they lack unified taxonomies and evaluation standards. After examining diverse deep learning models, we found that each technique has unique advantages and limitations. Selecting an appropriate model according to data characteristics is crucial. This chapter also outlines the thesis structure and summarizes our contributions. Chapter 2 provides an overview of generative time series models. We propose a classification system based on fundamental deep learning frameworks, categorizing generative models into four types: Neural ODE–based models, RNN-based models, VAE-based models, and GAN-based models. We further analyze the strengths and applicability of each category for trajectory simulation tasks. Chapter 3 focuses on generative models for anomaly detection. After introducing the background and application scenarios of time series anomaly detection, we review related work in spoofing detection and dynamic graph modeling for financial transactions. We then present our method combining generative time series modeling with dynamic graph representation learning. Key components include generative dynamic encoding, pseudo-labeled graph construction, heterogeneous graph attention mechanisms, and optimization objectives. Experimental settings and ablation studies demonstrate the effectiveness of the proposed framework. Chapter 4 introduces an efficient trajectory simulation model. After summarizing related work on trajectory generation and large language models, we present our hybrid approach that integrates a graph neural network encoder with a large language model. We describe its architecture, prompt design, and inference process, along with experimental setup and comparative evaluations. Finally, Chapter 5 summarizes the thesis and discusses contributions to trajectory generation and road network prediction. This work systematically categorizes generative time series models, applies them to anomaly detection with graph neural networks, and proposes a novel hybrid GNN–LLM trajectory generator with strong potential for smart-city applications. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2025-01-01T00:00:00Z http://hdl.handle.net/10453/194769 Title: Mechanical characterisation of Wood-PLA composite materials fabricated by fused deposition modelling under water conditioning Authors: Xu, Weilong Abstract: Natural fibre-reinforced composites are gaining attention for their sustainability, particularly in additive manufacturing with Fused Deposition Modelling (FDM). However, the effect of natural fibre content on mechanical performance and moisture absorption on material integrity remains poorly understood. This thesis investigates the mechanical properties of pure PLA and wood-PLA composites under various conditions. Firstly, the mechanical performance of pure PLA and wood-PLA filaments in their unconditioned state was assessed. Tensile and single-edge notched bending (SENB) tests showed that adding wood fibre reduced tensile strength but enhanced energy absorption due to changes in fracture mechanisms, providing insight into fibre’s effect on failure behaviour. Secondly, the impact of water immersion and redrying on the composites was examined. Significant differences in strength, stiffness, and energy absorption were observed, with moisture uptake inducing reversible plasticisation, offering mechanical properties changes of wood-PLA composites in humid environments. Finally, wood-PLA filaments with varying fibre content were produced via melt-extrusion blending. These filaments were used to fabricate tensile specimens to investigate how fibre content influences mechanical performance in dry and moisture-conditioned states. The results demonstrated the correlation between fibre content, strength, energy absorption, and the extent of water-induced plasticisation. This research contributes to sustainable FDM composites for engineering applications. Description: University of Technology Sydney. Faculty of Engineering and Information Technology. 2025-01-01T00:00:00Z