The availability of free satellite images in medium/high special resolution has enabled possible solutions for the challenging dynamic land use and land cover mapping problems. This project is about developing new AI techniques for crop detection and mapping. Crop detection is the first step in AI-based time series analyses, aiming to provide fundamental information for many socio-economic applications. Examples are crop control and yield estimation, change monitoring, supply chain and food security, climate change policies such as crop rotation, insurance, and fertilization services. The lack of ground truth data is a major problem for crop detection. That is the case for most time-series analyses of historical data. On the other hand, the crop-specific variations in visual and chemical characteristics during a year are sensed by spectral satellite images. Therefore, this project is focused on developing AI techniques effectively utilizing the spectral bands for crop detection. This is achieved based on (1) an unsupervised framework to identify effective spectral bands for time-series analysis and crop detection. For this aim, the crops fingerprints and other vegetation indexes are used to identify the important spectral wavelengths. (2) a supervised framework for developing a novel spectral attention model using visual transformers prediction strategies.

One of the major knowledge gaps in the study of complex aerospace systems is the frictional interface of jointed components. In the current state-of-the-art, only the nonlinear friction is considered. For the underlying physics, frictional energy dissipates energy via deformations (using current methods), heat (not considered and is the focus of this project), and sound (considered ignorable for most sliding situations). This project will focus on the understanding of the heat-based energy dissipation and the environmental effects on these frictional joints. Swansea University is a member of the International Committee on Joint Mechanics. This is a multi-disciplinary committee focused on understanding and predicting jointed interfaces in assembled structures. The ICJM is a collection of academics, industrial researchers, and governmental bodies from across the world. This allows for knowledge transfer between industrial needs, current state-of-the-art, and relivant regulations. The successful applicant will have the opportunity to attend joint community meetings, where they can discuss and present their work, as well as attending and presenting at international conferences. This project will investigate the recently identified need (discovered by the ICJM) for multi-physics understanding and modelling. Specifically, this project will investigate the thermal-mechanical relationship in assembed structures. The student will utilise a newly acquired environmental chamber to understand these previously not investigated aspects of these nonlinear joints. In addition to the testing, the student will also work towards developing a novel temperature-dependent nonlinear joint model.

Arts, Activism, and Accessibility: Disability Arts in Wales, 1980-Present

Background: ProColl is a supplier of bovine and recombinant collagen for application within medical devices, cell culture, tissue engineering, pharmaceuticals, and cosmetics. The company was founded as a spin out from Swansea University in 2018 to bring to market the improved scale production of collagen with exceptional quality and purity. The company then developed techniques to produce recombinant collagen to answer the market need for animal free collagen that is more biocompatible, ethically robust and removes the risk of interspecies transfer of disease. Collagen is the most abundant protein within the human body and plays a central role in the maintenance and repair of all organs and tissues. Collagen has a structural role as the glue that anchors and houses cells within the extracellular matrix of tissues. Thus, it is one of the most industrially important proteins with applications as a functional, structural material in medicine, cosmetics, and food. Within medicine and cell research collagen is predominantly used as a gel or a coating; the collagen is used to coat cell culture materials to allow adhesion and subsequent development of the cells. Through collaboration with Swansea University, ProColl currently produce recombinant human Type I collagen molecules with the view to expand this to other collagen types. The research of the project will develop advanced materials in the form of new recombinant collagen materials and novel collagen formulations that are optimised for the coating of surfaces and application within cell culture, tissue engineering and wound healing. The recombinant collagen will be produced through fermentation in a sustainable process that removes the need for bovine sources and their accompanying impact on the environment. In addition, alternative raw materials for the fermentation will be investigated to further improve sustainability. The collagen surfaces will be characterised in terms of coating film morphology, biocompatibility and mechanical resilience using advanced techniques including atomic force microscopy, scanning electron microscopy, dynamic and fatigue testing systems, and cell culture. The project will examine different coating methods such as layer by layer, lyophilisation, spray coating, casting and electrospinning to control the morphology and functionality of the collagen coatings. Project Aims: The outcomes of the project will be the creation of new and improved processes for the manufacture of recombinant collagen. A range of novel surface coatings will be developed that are optimised for application within research and medicine. The research needed to achieve these outcomes will provide comprehensive and novel insights into collagen materials which is of interest to the academic community and will be published. The research will also be disseminated at key international conferences. ProColl will commercialise the new processes and products creating industrial impact and benefit to Welsh and UK economies.

The primary goal of this research project is to enhance the safety and comfortability of High-Speed Railway (HSR) infrastructures by addressing key challenges associated with their maintenance and operation during regular public transportation service. The project aims to answer the following critical questions: 1) How can deep learning techniques be applied to detect and assess structural distresses in High-speed Railway infrastructures, such as cracks in concrete slabs, more accurately and efficiently? 2) What are the best practices for using deep learning to monitor and predict foundation settlements that may compromise the stability and comfort of HSR systems? During the course of the project, an innovative integration of advanced deep learning techniques, including computer vision, Large Language Models (LLMs), etc. will be developed to address the challenges in HSR infrastructure engineering monitoring and maintenance: 1) Deep learning methods: This involves the development of algorithms capable of analysing images and videos of railway infrastructures to detect and quantify cracks, deformations, and other structural issues. 2) Large Language Models (LLMs): These models will be used to process and analyze vast amounts of vibration and settlement data related to HSR infrastructures under long-term service. Students involved in this project will undertake a variety of tasks, including: 1) Data Collection and Pre-processing: Gathering and preparing large datasets from various sources, including field inspections, historical maintenance records, and sensor data. 2) Algorithm Development: Designing and implementing deep learning models for detecting structural issues and predicting future maintenance needs. This will involve programming, model training, and fine-tuning. 3) Simulation and Analysis: Using deep learning methods to simulate the behavior of High-speed railway infrastructures under different conditions and validate the models against real-world data. 4) Integration and Testing: Developing an integrated system, and testing this system in a controlled environment before deployment on actual HSR infrastructure systems. 5) Reporting and Presentation: Documenting findings, preparing technical reports, and presenting results to stakeholders and the broader scientific community. This project not only aims to improve the safety and comfort of HSR systems but also seeks to provide students with hands-on experience in cutting-edge technologies, preparing them for future careers in engineering and data science.