Pathological wound healing is associated with ageing and many chronic diseases. It is one of the major medical burdens in the developed world, contributing towards leading causes of death and disease worldwide. The stages of wound healing normally progress in a predictable, timely manner; if they do not, healing may progress inappropriately to either a chronic wound such as diabetic ulcer or fibrotic scarring such as scleroderma. The prevalence of chronic wounds and fibrotic pathologies arrives at the number of ~80 million, which increases to >100 million if we include fibrosis associated with surgical procedures. Current treatments for chronic wounds and fibrotic scarring have limited success, so better understanding of biological mechanisms that promote healing whilst preventing scarring is urgent to achieve better therapies. Research carried out by scientists around the world as well as our own laboratory has recently discovered a crucial role for biological clocks in optimal wound healing and in curbing fibrotic scarring following injury. Biological clocks are timing mechanisms in our body that generate 24h rhythms in physiology and behavior, such as sleep/wake cycles, body temperature and hormone levels. They exist in almost all tissues and cells in our body. Their disruption due to shift work and ageing is a strong risk factor for chronic diseases. Our new pilot data using rodent models shows that there is a robust day/night variation in skin wound healing rates so that skin heals faster when injured during the night then when injured during the day. This faster healing at night is associated with increased levels of genes involved in production and organisation of matrix, a very important component for closing the wound. We have also discovered that antioxidant protection in our cells varies between day and night, and has an important role in faster night-time healing following injury. Even more, small, drug-like molecules capable of boosting antioxidant levels in our cells have a greater efficiency when used at the right time of day. These observations suggest exciting new ways to tackle pathological wound repair mechanisms. In this new project, we will answer the main question as to whether the biological clock present in key cells involved in wound healing (called fibroblasts) is critical for daily variation in repair capacity that we observed in our pilot studies. We will make use of two genetically modified rodent models 1) one in which we can visualise (by fluorescent tags) a matrix gene (called collagen) specifically activated in fibroblast cells and 2) the other in which we have genetically deleted the clock gene specifically in fibroblast cells. Using these unique rodent models, we will be able to monitor temporal changes in clock gene activity and matrix organisation within wounds. This research will uncover critical importance of robust clocks within key wound healing cells for time-of-day variation in healing rate and organisation of optimal wound repair. Furthermore, we will take advantage of advanced molecular biology tools to find out which genetic mechanisms biological clocks use to regulate matrix genes important in healing such as collagen. Using high-tech biochemical approaches, we will further find out whether biological clocks help our cells 'tell the time' when to produce the right amounts of antioxidants to fight off rises in dangerous free radicals following injury. Finally, we will test whether antioxidant-based chronotherapy (giving treatments according to one's body clock) has beneficial healing effects in chronic wound or scarring conditions such as diabetes and scleroderma. To test this, we will use rodent models of diabetes as well as skin tissue biopsies and cells from patients with Scleroderma. These new studies will provide crucial evidence to support future studies pinning down biological clocks as a new therapeutic target for the management of chronic wounds and scarring.

This project aims to improve fundamental understanding regarding the interaction of various applied flow fields, and particularly so-called "secondary-flow" vortices, with solid particles. A series of simplified geometries will be chosen to study a wide range of flow conditions for both Newtonian and complex fluids, in the presence/absence of vortices, and its interaction on the solids bed in a controlled and systematic manner. This knowledge will then be used to update or generate new models for the solid's transport, settling, bed formation and resuspension criteria. Analytical solutions concerned with Newtonian fluid flow in various geometries are readily available from many sources. However, the same cannot be said of viscoelastic fluids. One aspect of this project will look at developing analytical solutions for such fluids in a series of geometries that develop secondary flows. We will then further expand these solutions, where possible, by considering various flow conditions as well as the interaction of these flow fields with solid particles. Given the complex rheologies of the fluids concerned, coupled with the addition of solid transport, we will also be considering several numerical and experimental techniques, where appropriate. We anticipate that this will lead to the development of new models of industrial significance. Another aspect of the project will focus on the T-channel geometry for complex fluid flows. Whilst Newtonian fluids are an ideal model for analytical solutions - the reality is that most fluids are non-Newtonian. For such fluids, there is always a possibility of flow instabilities. The very presence of these instabilities adds a further complication to the problem. Given the complexity of the problem, we will employ the use of the numerical and experimental techniques available to us. Subsequently, we will then consider the same T-channel geometry with a solids bed. Given that the geometry with a complex fluid flow is already a relatively intricate problem, we plan to use a combination of numerical simulations and experiments for this particular problem; and we envisage that this will allow us to develop appropriate new models for industrial applications. In the interest of completeness, we will also consider a Newtonian fluid for a T-channel with a solids bed.

The Problem: It is estimated that 22% of adults in England are physically inactive, and these rates are higher within each Local Authority within the Liverpool City Region (LCR) (PHE 2019). Physical activity is an important determinant of health, being associated with lower risk of cardiovascular diseases, as well as improved mental wellbeing. Designing cities and neighbourhoods to encourage physical activity is therefore an important policy priority. An increasingly adopted approach is to increase the uptake of active travel, particularly cycling. However, only 3.3% of adults in England cycle for travel at least 3 times per week, and rates are lower for the LCR. Targeted investment in cycling infrastructure can encourage more individuals to take up cycling, as well as reduce air pollution indirectly benefiting health. The Solution: This project will use state-of-the-art machine learning and AI techniques to leverage new forms of data to improve decision making around cycling investment. Such approaches are rarely applied within transport modelling, but offer novelty to process and model complex (big) data to inform cycling behaviours and infrastructure provision. A key advantage of the PhD will be the development of bespoke methods that enable to make the most out of data unexplored in the context of cycling. These methods will be designed so that they can be easily deployed within any local government to inform cycling provision. This project is designed to co-produce real-world solutions alongside the non-academic partner, the LCR, thus maximising impact. Outline of the PhD: The project will be structured as a publication-based PhD, and will include three main subprojects: 1. Modelling volume of cycling traffic from pneumatic road tube counters This project will use cycling counts from pneumatic road tube counters and ancillary data about the characteristics of the locations where they are placed to build a predictive model of cycling counts at the street segment that can be deployed to the entire network of the LCR. This will enhance the understanding of the distribution of cyclists to agencies related a range of domains, from public health to transport planning. Methodologically, this project will expand tree-based models (e.g. random forests, boosted trees) to explicitly incorporate spatial features and relationships. 2. Understanding the drivers behind cycling flows This paper will unpack the driving factors behind the estimates obtained in the previous one. By combining traditional socio-economic sources of data (e.g. Census, Deprivation scores) with new approaches such as video footage or imagery data that recognise features of the environment (e.g. road quality, foliage, etc.), the study will identify how environmental factors interact with social conditions to determine the extent to which people cycle in different places. To be able to leverage these data sources, state-of-the-art AI techniques such as convolutional neural networks (CNNs) will be required. 3. Predicting where to invest on urban cycling infrastructure In this final paper, the student will use results from the previous two in order to build a decision-making system that informs policies on improvement of cycling infrastructure in the LCR. The system will fulfil two main functions: first, it will provide an intuitive way of visualising and interacting with the results of the predictive models generated and the measures of uncertainty associated with them; second, it will feature the capability of asking "what-if" type of questions around the improvement of infrastructure. In this context, the student will explore the suitability of spatial interaction and agent-based models. It is expected this system will enable the identification of policy priorities within the LCR.

The engineering of the metal/insulator nanostructures capable of harnessing THz energy is the central aim of this project. The challenge lies in tuning the barrier heights at the metal/insulator interfaces for optimal terahertz energy conversion. Instrumental in achieving this ambitious goal is thorough understanding of interfacial barrier formation and correlation of physical and electrical properties of proposed nanostructures. The nanostructures will be fabricated using atomic layer deposition (ALD). The ALD deposition system enables a controlled microstructure, leading to better uniformity and control of the tunnelling barriers' composition and thickness, as well as interface integrity and stability. The target is addressed in three coupled work phases, which are strongly linked, and entail voluminous theoretical and experimental study with a wide range of characterization techniques. The successful outcome of the project will facilitate an emerging technology and complement research efforts at Manchester University and Imperial College in the UK to bring about new efficient electronic devices for terahertz energy harvesting in infrared and visible domain.

1st Paper Abstract The UK residential sector is inefficient and has an overwhelming reliance on natural gas as a heating source. For the UK to meet its 2050 net zero obligations, the sector will need to go through a process of decarbonisation. Previous studies acknowledge the spatial disparities of household energy consumption, but have neglected how consumption varies over time. This paper advances such shortcomings via a sequence and clustering analysis to identify common gas consumption trajectories within neighbourhoods in England and Wales between 2010-2020. Four clusters are identified: "Very High to High Consumption"; "High to Medium Consumption"; "Medium to Low Consumption" and "Low to Very Low Consumption". The clusters were contextualised using spatial datasets representing the socio-economic and built environment. Across all clusters, the proportion of inefficient dwellings were high, but there was a trend of high consumption associated with lower proportions of energy efficient dwellings. The results provide useful insight to policy makers and practitioners about where best to target electrification and retrofitting measures to facilitate a cleaner and more equitable residential sector. Policy targeting of areas with continual high gas consumption will accelerate the decarbonisation process, whilst targeting areas who continually under consume will likely enhance household health and well-being. 2nd Paper Abstract The UK has a long history of observing energy vulnerability through fuel affordability and energy efficiency. This does not encapsulate the wider drivers that give rise to a household's inability to afford an adequate level of energy service. Research has filled this void through combining known drivers in the form of a composite index. However, these studies are temporally static, where monitoring progress and the identification of areas with entrenched energy vulnerability can't take shape. The consideration of time is a necessity as the physical and mental health implications from energy vulnerability are known to compound overtime. We fill such a void by designing a new spatial temporal composite indicator using socio-economic and dwelling measures at 2011 and 2021 within English and Welsh neighbourhoods. Our results outline a stagnation of energy vulnerabilities, with greater risk assigned towards urban areas. A sensitivity analysis allows us to adapt the weighting of vulnerability domains, with a select few neighbourhoods identified as continually vulnerable across all unique weighting combinations. Such areas have faced an extended period of deprivation, where we advocate policy targeting to raise living standards. 3rd Paper Abstract The Green Deal Grant was a government funded retrofitting scheme geared towards decarbonising the residential sector. Households applied for finance for energy saving measures, which would be paid off through the energy bill savings that the retrofit would yield. However, the studies which measure the success of the Green Deal using a host of energy related metrics has been limited to date. Using EPC data, this paper uses a difference in difference regression analysis to assess the impact of the retrofit on a household's energy consumption, energy efficiency and carbon dioxide emissions before and after the retrofit has been installed. The scheme was small in scale, but the overall results are favourable. Once the retrofit was installed the dwelling experienced a rise in energy efficiency, and a fall in carbon dioxide and energy consumption. Today the scheme is scaled back from a host of funding and uptake issues. However, we advocate policy to retrofit the UK housing stock on a mass scale to make a meaningful fall in the UK's carbon emissions to better reach net zero obligations.