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Reducing food loss and waste is essential to ensure food security and in achieving United Nations Sustainable Development Goal 12.3 and Sainsburys Supermarkets Plc sustainability targets. Controlled atmosphere (CA) is broadly used to delay ripening and senescence. It consists of reducing oxygen concentrations and increasing carbon dioxide levels in the atmosphere surrounding fresh produce during storage (Falagan and Terry 2018). However, the sudden change in the gas environment is perceived as an abiotic stress, negatively affecting quality. Graduated Controlled Atmosphere (GCA) was discovered in 2020 and has been shown to extend the storage life of multiple fresh produce types viz. blueberries, vine tomatoes and cherries. GCA gradually reduces oxygen levels in the storage atmosphere rather than instantaneously applying the final CA conditions. GCA has been shown to increase the storage life of blueberries by 25% compared to control by reducing disease incidence when. GCA blueberries were also 27% firmer than CA-stored fruit after 28 days of cold storage (Falagan et al., 2020). The mechanisms behind the improved effect of GCA vs. standard CA on storage life are not known, but it is hypothesized that GCA influences oxygen-dependent ethylene signalling, even in non-climacteric fruit. The aim of the work is to understand the mechanisms by which GCA works compared to standard CA from two perspectives: i) GCA effect on ethylene sensitivity (via low O2 and high CO2 graduation); and ii) natural disease resistance (via high CO2). This work will develop understanding on the relationship between low oxygen environments and the ethylene biosynthesis and abscisic acid (ABA) pathways in a dynamically changing gaseous environment. It will also study the progression of fungal disease (Botrytis cinerea) through microscopy and quantified by PCR.

The unmanned aerial vehicles (UAVs) are new types of user equipment connected to cellular networks with promising revenue through additional new subscribers and use cases, especially for aviation sector. In addition, UAVs are accepted as an extension to base stations by boosting coverage, spectral efficiency and user quality of experience. In this context, the main aim of this project is to provide high quality of data link and smooth UAVs connectivity into cellular network infrastructure. The UAVs allow rapid deployment of a multi-hop communication backbone in challenging environments with applications for public safety, delivery and monitoring. Therefore, Unmanned aerial vehicles (UAVs) can be used as complementary infrastructure to provide wireless services for the ground users or they may require wireless connectivity from the ground for a safe and reliable operation. This PhD project aims to study several UAV use-cases covering 5G core networks and to validate UAV/UTM connectivity KPIs for supporting such challenging. The project will drive UAV and 5G networks to a win-win position, on one hand by showing that 5G is able to guarantee UAV vertical KPIs, and on the other hand by demonstrating that 5G can support challenging use-cases that put pressure on network resources. To achieve that we will look to study of where 5G can add value to improve the existing UAV connectivity.

Accurately determining Airport passenger Flow (APF) is critical to efficiently manage airport processes and allocate resources and is fundamental to the provision of a seamless passenger journey experience. Currently, the estimation for APF have several limitations. One major limitation is the lack of explainable ability for the APF estimation model. An explainable network will allow the design of a transparency network that helps to clearly identify reasons behind the bottlenecks in airport operations and resources that relate to passenger flow, by providing a traceable inference process that enables effective decision-making to optimise operations. The PhD will demonstrate explainable time-series data modelling with the particular focus on flexible airport passenger flow prediction. The research outcomes will have significant opportunity to accelerate the development of smart airport.

Transportation of sewage can lead to the development of septicity. This transforms the character of the sewage and can lead to the formation of odour and methane emissions. Unfortunately, the impact of septicity on wastewater characteristic and how such changes influence treatment efficacy are poorly understood. Accordingly, this EngD studentship offers an exciting opportunity to explore a very common but massively under explored aspect of sewage treatment. The work will explore the transformations that occur and develop appropriate models of the changing character. The outcomes of this will be translated into impact on the efficacy of sewage treatment through a combination of laboratory testing, pilot plants, site testing and modelling. The work will be linked to analysis of TOTEX, life cycle analysis and robustness to establish the overall business case for implementation.