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The University Graduate School is offering PhD studentships for October 2022 entry. Science, Engineering and Computing projects from which studentship applicants must select are listed below (see project proposals); no other projects will be considered.
Applicants are strongly encouraged to contact the project's first supervisor to discuss their interest before making an application.
Using technology to enhance learning is an important aspect of modern higher education in the UK and elsewhere . With the arrival of the digital Internet age and its rapid advances in web and mobile technologies, the possibilities for managing knowledge involving the storing, publishing, accessing, querying and presenting of information digitally offer tremendous opportunities that are currently under-developed in the use for the classroom. When considering digital knowledge management, the organisation of knowledge as hierarchical trees (knowledge trees) is a natural approach with numerous benefits . For example, cyber security knowledge specified in a recent UK national initiative, the Cybersecurity Body of Knowledge (CyBOK), is available in form of knowledge trees . Hierarchical trees are equivalent to structured outlines consisting of a heading node and a finite number of children that are structured outlines themselves. Zoomable online outliners [4,5] are emerging tools, providing cloud-based access and storage of textual information in form of hierarchical outlines. They offer an intuitive user interface ideal for improving productivity through focus, task management and brainstorming. This is manifested through a lively community of users expressing their experiences in blogs such as the WorkFlowy blog and the Dynalist community. Using both Zoomable Online Outliners and knowledge trees together yields an innovative learning technology system, enabling cloud-based management and delivery of knowledge trees . This PhD project will investigate this unique and novel combination for learning and teaching in higher education, with a focus on the cyber security subject area.
The overarching aim of the project is to improve the presentation, interaction with and teaching of cyber security knowledge based on the ability to focus by hiding context in different hierarchies, to explore knowledge visually, and to query and extract knowledge easily.
The methodology for this project will be built on previous work carried out by the supervisory team, extending it to develop a formal learning and teaching methodology comprising of the entire document creation, presentation and maintenance cycle. Learning technology tools will be implemented based on enhancing existing Open Source tools, an existing web-based searchable representation of CyBOK Knowledge Trees , and Chrome browser plug-ins for the use of the ZOO tools WorkFlowy and Dynalist . Evaluation of the framework and tools will be based on a statistical analysis of data collected through using student surveys and questionnaires.
Applicants should have at least an Honours Degree at 2.1 or above (or equivalent) in Computer Science or related disciplines. In addition, they should have a good mathematical background, strong web technologies skills, and an interest in education.
First supervisor: Dr Eckhard Pfluegel email@example.com
Improvements in computer vision, and deep learning advancements, along with the introduction of smart devices and powerful computers, have stimulated research interest in the field of real-time scene analysis. Behaviour and face analysis is the process of describing the actions of a given person or a group of people by leveraging extracted information from their actions and the overall environment. Although various applications in the areas of surveillance, security and autonomous vehicles use human behaviour and face analysis as a key component, further performance increase is still required to allow their generalized deployment.
As a novel AI solution for integrated smart devices has the potential to offer such progress, this project aims to research scene analysis methods that could be integrated into small wearable devices. Specifically, the project will address a set of contemporary problems related to face detection, recognition, and human behaviour analysis with the goal to be integrated into low-power devices.
First supervisor: Professor Vasileios Argyriou Vasileios.Argyriou@kingston.ac.uk
For provision of correct medicine, treatment and correct therapy, an accurate diagnosis is required. There are situations in which time-critical decisions need to be made to provide the right treatment to avoid long term morbidity or in some cases even death. In England alone, cardiovascular diseases' (CVD) related healthcare costs amount to an estimated £7.4 billion per year, and annual costs to the wider economy being an estimated £15.8 billion. The UK government is focused on telemedicine, and in the next five years, with the help of the NHS, it plans to save at least 150,000 lives each year by avoiding/managing heart attacks.
The procedure of placing the electrodes on the human body and collecting the electric signals from a cardiac muscle during each cardiac cycle is known as Electrocardiography (ECG) and the obtained signal is known as ECG signal. The health of the cardiac muscle is commonly measured using ECG signals, which are used to measure; the rhythm and rate of each cardiac cycle, damage of cardiac muscle or conduction system, the function of inserted pacemakers in the heart, the effects of cardiac-related medications and the dimensions of the cardiac chambers.
To detect and classify CVDs, the time-sequence of ECG signals is carefully examined by a cardiologist or electrophysiologist. The detection of abnormalities in the ECG signals is stated as anomaly detection. The ECG time-series signals are composed of massive data comprising of fast cardiac rhythms and these vary from patient to patient, which makes anomaly detection a challenge. Computer aided anomaly detection in ECG signals and its correct classification is significantly important to identify dissimilar cardiac beats, which further results in detecting related CVDs through manual diagnosis performed by a cardiologist or electrophysiologist.
The existing anomaly detection approaches in ECG signals undergo a high false-positive (FP) and true-negative (TN) rate. The existing methods are not intelligent enough to distinguish between normal ECG signals and ECG artifacts. In terms of frequency or rhythms, the ECG artifacts which are quite similar with normal ECG signals are indistinguishable. Current research, which is mostly based on powerful deep learning networks i.e., long short-term memory units, generate high FP and TN rates because they focus only on clean signals without concerning noise or artifacts in signals. Ignoring the noise or artifacts in ECG signals is impractical and causes the outcomes to inappropriately ensure the normal cardiac cycle or its morphology, which is critical for further analysis of disease classification. The FP and TN rate of anomaly detection in ECG signals tends to rise substantially even in powerful deep learning networks, which remains a challenge for the reliable real-time implementation.
This proposal focuses on introducing smart ECG machines which can detect anomalies in ECG signals in real-time. Based on an advanced artificial intelligence (AI) aided signal processing models, these machines will be able to memorize the sequences of ECG signals. In order to classify the CVDs, the acquired abnormal signals will be further converted to spectrogram images for input to a 3D image classification model. One of the biggest hurdles in training an accurate AI-based disease classification system is the training data. There are several open-source ECG databases available all across the globe which are free to use and contain tens of thousands of real-time ECG samples.
First supervisor: Dr Arslan Usman M.Usman@kingston.ac.uk
The increase of global warming and pollution is a real danger to the survival of millions of species in natural habitats around the World. This is a real threat, for which the European Union has adopted deeply transformative policies detailed in the European Green Deal. Among these, a prominent role is given to the restoring and preservation of ecosystems by increasing the coverage of protected biodiversity rich land and sea areas building on the Natura 2000 Network.
At present, human operators are the only option to perform the monitoring of such a large area, because of their specific expertise and knowledge in identifying plants and assess threats to a habitat, and their ability to move, explore and assess in wild unstructured environments such dunes, forests, and mountains.
The reality is that the availability of botanists, as human operators, has rapidly decreased during the last decades. The artificial alternative is robotics, with an agile robotic platform carrying a varied and intelligent array of sensors. Indeed, robotics has made tremendous advancements in recent years, however robots hardly leave laboratories and factories because they are not robust and efficient to survive in the real world. Robot intelligence is also a clear limitation to what is required in botanical field work. The topic of this PhD course of studies will be mainly concerned with the development of artificial intelligence methods for the automatic recognition and identification of the health of a habitat. This will be an integrating aspect of the H2020 Natural Intelligence European project (https://www.nih2020.eu), in collaboration with European botanists and roboticists.
The program of work will entail an in-depth study of the existing methods used for the identification of a small set of key indicators of the health of a natural habitat. This could be the amount of green areas or the presence of a plant, either positive (for instance a wild orchid) or negative (lack of green areas) to the habitat.
Research will then have to be carried out to fill the state of the art gaps, employing the latest machine learning and image interpretation methods. The development will exploit new large datasets collected by the botanists in four European very different habitats (dunes, prairies, forests and mountains) and the latest deep learning techniques, employed for identification, disambiguation and recognition of the sought for species. Ultimately, data from the natural habitats will be collected semi-automatically with the ANYMAL robot (https://rsl.ethz.ch/robots-media/anymal.html), with an on-board sensor array, inclusive of a camera in the visible and non visible spectra, LiDAR, humidity and temperature sensors. The richness of the sensor array will allow the development of multimodal algorithms, making an innovative contribution to research in a number of fields, such as image and video interpretation, artificial intelligence, machine learning, pattern recognition, botany and robotics.
The project activities will include research into AI algorithm development, liaison with external partners and presentation of work at project meetings, technical sessions, and scientific meetings.
First supervisor: Professor Paolo Remagnino P.Remagnino@kingston.ac.uk
Technological firms are regarded as key for national and regional economic development. Today, more than ever before, the business environment is burgeoning with innovation. Even simply Googling for apps and services, to help boost elementary office efficiency, returns over 2000 items.
In an organisation, implementing a beneficial innovation results in benefit "I" minus costs "C", but implementing a ‘bad' innovation is very costly (i.e. minus [I+C]). Without spill over and without hierarchy in an organisation that inhabits an environment that contains many ‘bad' innovations; fads and fashions can circulate unchecked, and these detrimental ideas can swarm into the organisation and resemble epidemics, harming the organisation. Adding the simplest form of hierarchy, one manager who makes decisions about implementing innovations by flipping a coin, means the loss is halved compared to the flat organisation. However, successful high-tech firms are paradoxically well-known for being flat organisations.
This project investigates this important enigma because SMEs and other start-ups are major sources of innovation and employment. With intensified competition and globalisation, the imminent prospect of recession, national and regional Governments are increasingly looking towards being able to create high-tech ‘knowledge ecosystems' to promote innovation and increase financial growth.
This work will model factors leading to success or failure of tech firms and their clusters, Science and Technology Parks, using a combination of classical techniques like GIS, mining standard open source (e.g. ONS) data and commercial business databases. It will rely largely on econometric techniques including SEM, Markov Chain and kinetic Monte Carlo methods, using e.g. Python and R.
First supervisor: Dr Rob Mellor firstname.lastname@example.org
The emergence of unusual or unexpected distributions in large data samples has resulted in some well-known laws, which includes Zipf's Law in textual analysis , the Pareto distribution in the measurement of wealth , Benford's Law in the distribution of first digits in real-world measurements , and Chargaff's Second Parity Rule in genetics . More recently, powerful arguments based on a synthesis of classical statistical mechanics and information theory have shown how such distributions can arise naturally in what appear to be otherwise unrelated systems or areas of study  and which are scale independent. What these systems have in common is their capacity to be represented by symbols that carry no intrinsic meaning other than their ability to be distinguished from each other.
We propose in this project to explore further the emergence of such global properties in appropriate databases, beginning with publicly accessible databases of proteins. The aim of such explorations is to gain some insight into the departures from the expected equilibrium distributions, and to determine whether metrics can be produced that provide information on the size of the departure from equilibrium. It is expected that the outcomes of this work will have applications in a broad range of areas, including molecular biology, economics, ecology, biology, organisational structures and beyond.
The project will require strong analytical and computational skills, and would particularly suit a graduate in one of the following areas: Computer Science, Theoretical Physics or Physics, Theoretical Chemistry, Mathematics or Applied Mathematics, Economics, or equivalent. A background knowledge in this area is not required, but enthusiasm and the patience to work with large datasets is essential, along with a drive to produce efficient programming code, so a programming background in any language, or range of languages, would be helpful.
First supervisor: Dr Gordon Hunter email@example.com
With the development of deep learning approaches and convolutional neural networks (CNN) in particular, the task of recognising objects from an image has become associated with the ability to train a network using a large number of labelled images for each class of interest [He2016]. The creation of large, labelled datasets such as ImageNet [Ru2015] has permitted the development of ever more performing architectures. In order to extend further the number of classes to which objects can be recognised without requiring to have access to a large number of labelled images of the new classes, few-shot learning (FSL) and even one short learning were proposed: in such frameworks, the initial training set is completed with a support set containing only a few images to define each additional object class [Sung2018]. Eventually, reliance on training images for new classes has been totally removed; instead, textual attributes proved sufficient. By learning a mapping between object images and textual attributes using a large training set, an object from a new class only defined by its textual attributes could be recognised. While initially zero-shot learning (ZSL) focused on identifying images belonging to the unseen classes of interest, generalized ZSL offers predictions for both the seen and unseen classes [Ji2021]. Although textual attributes can be retrieved automatically from the known labels using resources such as word2vec [Ch17] and Wikipedia, ZSL is essentially a retrieval problem as labels of all classes of interest must be known. Unfortunately, such constraint can only be met in a limited number of scenarios, such as those associated to existing specialised datasets dedicated to specific applications, e.g., ‘bird watching' [Wa11]. Here, it proposed to replace this constraint by a less limiting one, i.e., access to an internet connection (or at least an electronic copy of an encyclopaedia). In such scenario, in principle, picture of any not only unseen, but also unidentified object can be processed and labelled. For example, if a system were able to characterise an unidentified object in an image as ‘a beaver with a duck beak', a simple query using one's favourite search engine would be sufficient to label the object as a ‘platypus'.
The aim of the project is to develop an efficient deep learning-based pipeline allowing the annotation of any object present on an image without prior knowledge by the system of their existence. Taking advantage of extracted visual features to create an Unidentified Featured Object (UFO), and existing mapping between visual and textual features, natural language processing techniques can then be applied to generate a list of putative annotations. Eventually, they can be analysed using an FSL-based framework to identify the most likely label.
Applicants should have an honours degree at 2.1 or above (or equivalent) in Computer Science or a related discipline. In addition, they should have a good mathematical background, excellent programming skills in Python, and an interest in machine learning.
First supervisor: Professor JC Nebel J.Nebel@kingston.ac.uk
Museums and art galleries across the world are not only buildings that house specialised objects, artworks, research, they are a curation of earth history and the beings and nature that reside; thus, they play an important role in our society. In the UK, there are 2,500 museums and galleries, 1,500 are accredited , that amass millions of visitors each year. These non-profit institutions seek to offer enjoyment but also act as a space for education, inspiration, and skill exploration with an aim of continuing the spirit and enthusiasm of the eighteenth-century UK government goal to provide public learning equality and access . These institutions seek to garner the attention and attainment of two types of visitors, physical (on-site, footfall) and digital (online through websites, apps, and social media) that are accessible to all, from children, older people, and those with disabilities and conditions . Over the last two decades physical museum and gallery inclusivity has improved tremendously, especially with the rapid advance of textiles and digital technology (e.g. [4, 5, 6, and 7]), although, these experiences are often less equal for visitors who are blind or partially sighted, the design and execution of audio descriptions, tactile books and sensory artefacts and technology has greatly improved experiences (e.g. [8, 9, 10, and 11]), however, onsite museum and art gallery access, interaction, and knowledge exchange is far from addressed most notably the recent closures of sensory interaction points because of Covid-19 health measures . There are two million people registered as blind and partially sighted in the UK , thus this research seeks to improve museum and gallery experience for a substantial number of individuals.
The aim of the project is to improve on-site museum and art gallery experiences for blind and partially sighted visitors. The successful completion of the project requires addressing the following scientific objectives:
Applicants should have, at least, an Honours Degree at 2.1 or above (or equivalent) in Human Computer Interaction or User Experience Design.
First supervisor: Dr Makayla Lewis M.M.Lewis@kingston.ac.uk
The global voice recognition market size is forecast to grow from 10.7 billion U.S. dollars in 2020 to 27.16 billion U.S. dollars by 2026 with a compound annual growth rate of 16.8% during the period . Moreover, all the main technology manufacturers, including Apple, Google, Xiaomi, Amazon, IBM, and Samsung, are already major players in producing voice communication devices. As voice over devices becoming norm, there is a need to decipher emotional aspects of conversations.
While sentiment scoring has been used to determine primary emotions of the users from these conversations, secondary emotions have largely been ignored as they are much more complex to detect, and they are often masked by the primary emotions. However, including secondary emotions is crucial to build systems that incorporate "human-like" behaviour. In addition to discerning facial expressions, it is imperative to incorporate emotional conversational analysis to reality technologies that are used by Robots, Voicebots, and Chatbots.
Using Robert Plutchik's  emotional wheel, primary and secondary emotions can be evaluated to learn more about emotional aspects of a voice chat or conversation. Although speech recognition frameworks like natural-language understanding (NLU) and processing (NLP) are capable of elucidating user intent, they cannot decipher emotions. This research intends to use voice over chat conversations or conversations from voice devices to provide intelligent conversational analytics solutions for various applications leading to building smart notification systems. In particular, such system could be used to detect vulnerable users needing help, which could otherwise not have been identified by traditional digital behaviour analysis. Indeed, we identified this important limitation in preliminary research performed as part of an experimental study that can be adapted to reality technologies in everyday activities .
The aim of this project is to design a framework that uses machine learning algorithms on real-time data to determine emotional behaviour so that it can be used for various purposes like digital profiling, mental health awareness, and detecting threating, violent and abusive behaviour.
Applicants should have, at least, an Honours Degree at 2.1 or above (or equivalent) in Computer Science or related disciplines. In addition, they should have excellent programming skills in Python, and an interest in machine learning.
First supervisor: Dr Jay Jayaramakrishnan J.Kiruthika@kingston.ac.uk
Neuromorphic vision sensors enable the acquisition of scenes with limited energy requirements and low data rate, only acquiring information on what changes in the scene, with a behaviour similar to the one of the human eye. The acquired information can be easily exchanged in internet of things scenarios where battery operated visual sensors are connected to monitor a scene or perform a task requiring limited delay (e.g., tele operated driving).
On the opposite direction, light field capturing and visualisation involves the acquisition of information of a scene from multiple angles / with multiple microlenses and the rendered content is visualised as holographic content, resulting in a truly immersive experience, but also huge data rates.
The project will study compression methods for the data acquired with such methodologies, as well as subjective and task-based criteria for quality assessment of the compressed information. The latter task may leverage machine learning strategies, hence a background in the areas of machine learning is desirable. Also, edge-computing supported compression and processing strategies will be addressed.
Data compression at smart edge/fog-based gateways is beneficial, as data compression can be too computationally intensive to be performed on the acquisition sensor/location.
The possibility to acquire information from multiple neuromorphic vision sensors will also be considered, as well as application in real life scenarios (e.g. smart cities, healthcare, gaming).
Collaboration with national and international partners (research bodies/academy/industry) is expected, as well as the possibility of an industrial internship during the PhD.
The project will benefit from the state-of-the-art equipment available in our Centre for Augmented and Virtual Environments (CAVE lab) including neuromorphic vision sensors, plenoptic camera for the acquisition of light fields, autostereoscopic and light field displays.
Candidates are expected to have at least a BSc 2:1 in a relevant area (computer science, engineering, statistics, mathematics) and possibly an MSc in a related area.
First supervisor: Professor Maria Martini M.Martini@kingston.ac.uk
Type 2 diabetes is an increasing public health problem, affecting 1 in 10 adults globally, and is a major cause of premature death. The number of adults with diabetes has quadrupled over the last 30 years and is projected to rise to over 500 million by 2030. Early detection and prevention both of Type 2 diabetes and associated cardiovascular disease is key to improving the health of a population. Identifying those people at high risk of disease could be improved by incorporating information from retinal fundus image analysis. Blood vessels which can be viewed on the retina are connected to the microvasculature of the body, and therefore offer a view of the general health of the blood vessel system. Growing evidence in the field links morphological features of retinal vessels to early physiological markers of disease. This research will focus on predicting Type 2 diabetes and cardiovascular disease risk from information in retinal fundus images.
Recent advances in artificial intelligence applied to imaging problems can be used to advance healthcare in terms of generating efficiencies from automated assessment of images and also by providing opportunities for prediction of disease. The project will utilise a range of data sets that will include both cross-sectional cohorts and longitudinal studies. Machine learning approaches to analyse precise automated measurements of vessel shape and size will be explored and the proposed work will enable analytical tools to be developed across artificial intelligence and computer vision fields of research. The work will explore deep learning methods to assess risk of disease within various cohorts from image data. Architectures that exploit the strong spatially local correlation present in natural images will be assessed. Visualisation techniques will be used to provide interpretability of decision making, and attention mechanisms will be investigated with the aim of creating explainable and trustworthy models. The generated models may also be applied to other application areas in healthcare that would benefit from more accurate disease risk prediction and extended interpretability from image data. The project will complement other current work in the team by researchers that are exploring related approaches.
The project team will include computer scientists from the School of Computer Science and Mathematics, Kingston University, and clinicians, epidemiologists and statisticians from the Population Health Research Institute, St George's, University of London. The project activities will include research into AI algorithm development, liaison within the research team and with external partners, and presentation of work at project meetings, technical sessions, and scientific meetings.
First supervisor: Professor Sarah Barman S.Barman@kingston.ac.uk
In the last few decades, image sensors attached to airborne platforms, UAVs or satellites have evolved making possible the capture of high-resolution multi-spectral images. As a result, remote sensing object recognition and classification has become a widely-studied field providing solutions on the detection and classification of buildings, other structures, land and air vehicles. As a consequence, many remote-sensing applications, such as city planning, urban mapping and urban change detection have progressed by using object detection systems that employ aerial images and reconstructed 3D representations. As there is a lack of annotated real data, an important aspect of the project was the creation of a data generation system supporting fully 3D scenes and data models in order to produce rendered annotated images and different background environments.
This project is motivated by generating training data in order to obtain high performance object detection and recognition. It is also focused on the identification of the best existing methods for scene analysis and data augmentation. It aims to produce more realistic 3D training data to ensure the delivery of systems addressing specific scene analysis requirements. In light of those results, this project will offer solutions to improve the quality of the generated data and the overall performance of the object detection and recognition solutions by enhancing data through a pre-processing stage, supporting multi-scale object recognition, and offering a data generation tool supporting full multidimensional rendering.
The general aim is to offer further functionalities and features allowing data generation for more advanced scenarios covering all the scenarios and applications that are related to scene analysis.
First supervisor: Dr Jarek Francik Jarek@kingston.ac.uk
This proposal is part of the Data-mdapps project which is a collaboration between Kingston University and the WHO European Office for the Prevention and Control of Noncommunicable Diseases (NCD Office) and focuses on addressing the challenges of developing a technical and security policy framework for digital health behavioural data processing. Data-Driven Decision Making (DDDM) refers to the practice of basing decisions on the analysis of the data rather than purely based on intuition. In the early days, decisions were made based on intuition, but with the popularity of the big data culture, the emphasis has shifted towards the data.
DDDM is important for global organisations such as the WHO, since their decisions affect millions of lives. Therefore, their decisions need to be efficient, effective and ethical. Further, they are accountable for the decisions they make. Hence their decisions need to be strongly backed by data. In order to make data-driven decisions, data needs to be collected, cleaned and analysed in such a manner that the decision-makers are aware of the true picture represented by the data. In other words, decision-makers should be aware of the various biases such as sampling bias, outliers in the data and seasonal trends.
At present, the WHO is actively using data in its operations, both in internal decision-making and in educating the general public. For example, a framework named Evidence-to-Decision (EtD) supports the decision-making process. In addition, various dashboards, such as the covid dashboard, are used to educate the general public. Further, the WHO NCD Office is actively researching in digital health behavioural data processing.
First supervisor: Professor Christos Politis C.Politis@kingston.ac.uk
In recent years, the Internet of Things (IoT) has progressed dramatically due to advancements in technology. It plays an important role in the development of abundant applications and economic ventures. In IoT, smart devices communicate with each other using the Internet without any human intervention. The 6G vision also considers the massive IoT as a driving force, in which a strong relationship has been identified between 6G and high-performance mobile edge computing . While edge computing resources will handle some of the IoT and mobile device data, much of it will require more centralized resources to perform the data processing task.
To execute the big data analytics generated by IoT devices easy, the whole IoT network can be divided into different subnetworks and data of each subnetwork is collected, aggregated and forwarded by their respective central nodes. In such cases, the presence of malicious nodes causes severe issues in the sensing results, localization and service provisioning, which discourages new entities to join the network. Therefore, it is very important to establish trust between all entities by detecting and removing such nodes. Moreover, for reliable service provisioning in IoTs, the service provider nodes deliver services to the client nodes. However, there is no mechanism to collect enough information that assures the non-repudiation of both service provider and client node in the service provisioning mechanism. A blockchain-based localization mechanism is proposed for resource-constrained IoT nodes to find their location . However, there is no mechanism yet to prevent the client nodes from repudiating about actually demanded services.
The federated learning for the detection of malicious nodes will be further studied, which uses SVM and RF classifiers for identifying malicious nodes . In terms of implementation, the data of the IoTs network is initially collected by the respective sink node and then provided to the virtual machine associated with it. The model on this virtual machine is trained using the distributed dataset. After the models' training, the trained models are sent to the B5G fog server. The B5G fog server fuses all the models and sends the fused model back to each virtual machine. Then these virtual machines use this model for the classification of legitimate and malicious nodes of their clusters.
The project aims to investigate the distributed AI and implement the federated learning techniques in the dense IoT network in which the malicious nodes are detected in the network without compromising the privacy of sensing data. Federated Learning enables the IoT's data to remain on the device, the model will be further investigated to articulate how it learns with time and with the collaborative effort of all distributed IoT devices as agents.
Applicants should have, at least, an honours degree at 2.1 or above (or equivalent) in Computer Science or a related discipline. In addition, they should have a good mathematical background, excellent programming skills in Python/MATLAB and an interest in machine learning.
First supervisor: Dr Deepak GC firstname.lastname@example.org
Diabetes mellitus is a common cause of morbidity and mortality across the globe. Type 2 diabetes continues to rise in prevalence annually; from 1980 to 2004, Type 2 quadrupled in both prevalence and incidence . This will be exacerbated further by the increase in obesity. It is estimated that diabetes will be the seventh leading cause of death worldwide by 2030 .
Type 2 diabetes has a genetic influence, but is also strongly linked with food consumption and a sedentary lifestyle. The costs of managing diabetic complications currently outweigh the costs of anti-diabetes drugs by a factor of 3-4 . Research has shown that improved self-management could be a powerful tool in reduction of morbidity and mortality from type 2 diabetes, whether from prevention or improved glycaemic control. Research that can inform policy and guide management has the potential for large cost savings and to make a substantial impact on public health.
Big data collected from Diabetic individuals over the years can be used to provide personalised treatment plan to such individuals and informed decision support to healthcare providers and policy makers. Type of such data includes:
The increasing use of real world data will ensure that the evidence applies to the patient group in question. Factors peculiar to a particular ethnic group, locality or institution will be rapidly detected. This will enable the benefits of research to apply to the entire patient population and will particularly benefit ethnic minorities whose relevant genetic characteristics or other factors may differ from the general population.
Artificial intelligence (AI) along with the increased availability of health data hold great potential to pave the way for personalised prevention and enable progress towards risk prediction and early detection of chronic non-communicable diseases
Development of AI tools to explore such data and identify insight and knowledge to personalize treatment plan and prediction of complications to support clinicians while treating their patients can result in better health outcomes and reduction in cost. AI tools can also be developed to be part of the self-management apps that the patients use to support in managing their lifestyle and diet decisions. AI tools can also be used by policy maker in planning for the management of resources for such diseases.
Big Data projects require an appreciation of both the ethico-legal milieu and the socio-political landscape. Addressing such aspects is vital to develop trust in using AI tools in the healthcare domain.
The developed AI tools will be based on the FAIR  data principles and the good practices for GDPR-compliant personal data protection. The solution will also be based on international standards and best practices used in the development of AI solutions.
First supervisor: Dr Nada Philip N.Philip@kingston.ac.uk
Within the sphere of cancer chemotherapy, many commercialised drugs have been obtained by the synthesis of new compounds, from natural sources or by structural modification of natural products. The objective of cancer chemotherapy is to kill cancer cells with as little damage as possible to normal cells. An alternative source of anticancer drugs are natural products, which frequently seem to be more effective and/or less toxic. Screening of medicinal plants on the basis of their folklore or traditional use in the treatment of tumors has revealed some remarkable discoveries in the past. A bioassay guided fraction of many Croton species presented activity against different kinds of tumor cell lines with the cytotoxicity of the isolated compounds is the most studied. Hence, ethno-botanical and ethno-pharmacological research is crucial in the discovery and development of drugs from natural sources. Real information about the identification of plant species, part of plant used, preparation and dosage form of a folklore remedy, traditional use, and preservation of medicinal plants facilitates the search for new drugs, and the time needed for drug development programs.
Diterpenoids are characteristic components of the Croton species. Bioactivity of C. barorum (an African Croton) exhibited 100% cell inhibition at 10 µg/mL against the murine lymphocytic leukaemia P388 cell line with observed activity was due to crotobarin, a 3,4-seco-atisane diterpenoid. The leaf extract of Croton haumanianus J. Léonard, which has kauranes and clerodanes diterpenoids, showed selective activity against three of the NCI 60 cancer cell lines, the colon (HCT-116), the melanoma (M14) and the renal (786-0) cancer cell lines at a concentration of 10-5 M. Studies showed that the dichloromethane extract of leaves of C. zambesicus showed in vitro cytotoxicity against human cervix carcinoma cells. Moreover, the red latex of C. lechleri has been shown to have anti-tumor activity. C. lechleri sap possess antimutagenic properties and may inhibit the proliferation of human leukemic cells. Labdanes from C. oblongifolius have showed non-specific, moderate cytotoxicity against human breast ductal carcinoma (BT474), human undifferentiated lung carcinoma (CHAGO), human liver hepatoblastoma (HEP-G2), gastric carcinoma (KATO3) and colon adenocarcinoma (SW620) tumor cell lines. t-DCTN and its synthetic derivative dimethylamide-crotonin (DCR) inhibit HL60 cells growth in vitro partly by apoptosis induction and cell differentiation, but do not cause serious damage to immune cells, also t-DCTN showed strong antiulcerogenic activity. Taspine, an alkaloid that is active against KB and V-79 cells, a component that could be linkedfor the purported anticancer activity of C. lechleri red sap to thepositive results in antitumor tests using breast carcinoma and hepatoma. Anethole, a phenyl-propanoid constituent of volatile oil of C. zehntneri, has been shown to have anti-carcinogenic effect.
The aim of the present investigation is to evaluate the anticancer activity of (at least) one of Croton species, Croton fishlockii, based on folklore use in the treatment of different types of tumors and inflammatory conditions, and to explore and identify the potent chemical constituents from these plants responsible for antiproliferative activity against human cancer cells. The project will provide training in a wide range of chemical extraction and analysis, cell, molecular biology and analytical techniques such as Immunoblotting, Immunofluorescence, confocal microscopy, PCR, scratch assay and stable isotopic tracer methodology.
Recent extensive research work by Dr Huda Morgan showed very promising results of using Phoenix dactylifera; Ajwa dates variety as an alternative therapy to manage neoplastic diseases being effective and a low-cost prevention protocol, especially with the rise of cancer cases due to the impact of Covid-19 on the economy. Ajwa date variety shows to contain phytochemicals such as phenols and flavonoids that exhibited simple, yet effective biological anti-neoplastic activities. Cell culture on colon adenocarcinoma cells showed very promising results with an almost complete inhibition on migration in this cancerous cell line, which has facilitated further research on these compounds in the context of their use as a natural therapeutic in cancer patients by incorporating it into modern medicine due to the presence of series of anti-cancer phytochemicals which can aid in preventing the diseases. NMR confirmed the presence of Rutin and Quercetin from the use of 1D 1H, 2D 1H COSY and 2D 1H TOCSY experiments and both 1H and DEPTQ NMR spectroscopy of the DCM extracts also confirmed the presence of phenolic compounds as well as fatty acids and triterpenes.
Applicants should have a first or upper-second class honours degree in a relevant area to the project. A masters degree or equivalent qualification or other evidence of research skills and experience is preferred but not essential.
First supervisor: Dr Huda Morgan H.Morgan@kingston.ac.uk
Cannabis sativa, commonly known as marijuana has been found to contain 525 natural components that fall under several chemical classes. Cannabinoids fit in the chemical class of terpenophenolics and 104 of them have been identified so far (El-Alfy et al., 2010, Lafaye et al., 2017). Δ9-tetrahydrocannabinol (THC) is the most active component of the plant due to its high potential and profusion in plant preparations (Velasco et al., 2012). Studies have shown that THC mimics the endogenous substances, anandamide and 2-AG, by binding to the CB receptors inducing different pathways, eventually leading to the reduction of tumour growth (Pertwee, 2008).
Other distinguished cannabinoids also exist such as cannabidiol (CBD), cannabinol (CBN) and cannabigerol (CBG) that exert anticancer activity of which an important feature of CBD and CBG is the lack of psychoactive effects , (Scott et al., 2014). The non-psychoactive cannabinoids have minor attraction for the CB receptors hence they do not elicit their activity through these receptors. Instead, CBD induces apoptosis by the possible mechanism of induction of oxidative stress through the accumulation of reactive oxygen species (ROS) (Massi et al., 2006).
In 1981, a synthetic analogue of Δ9-THC was licensed for the inhibition of vomiting and nausea-induced from chemotherapy and in 1992 it was used as an appetite stimulant (Pertwee, 2009). In 2005, one more cannabis-based medicine, Sativex, entered the clinic containing similar amounts of Δ9-THC and CBD and is used by adult patients with advanced cancer as a complementary analgesic treatment (Pertwee, 2009).
The function of the endocannabinoid system in tumour generation and development has gained a lot of interest in the last decade. Malfitano et al. (2011) showed that overexpression of endocannabinoids and their receptors is correlated with cancer and tumour aggressiveness. Cannabinoids have been explored for the treatment of variety of conditions such as, fear and anxiety (Murkar et al., 2021), cancer (Kyriakou et al., 2021), dermatologic conditions (Sivesind et al., 2022), and respiratory diseases (Kicman et al., 2021).
Our group has extensively studied functionalised polysaccharides nanocarriers as a means of overcoming the blood-brain barrier. As part of our extended studies, we will also consider some of the polymers suitable to form films which might prove useful in delivering the active ingredients considered in this study.
This study aims to develop nanoparticles of in-house modified polysaccharides with CBD and THC, to achieve a high loading capacity and targeted anti-cancer drug delivery. In vitro testing will also be utilised for determining the passage of nanoparticles through the BBB and their functionality.
First supervisor: Dr Gianpiero Calabrese email@example.com
Research into novel antimicrobial agents has never been more pressing with the ever-increasing resistance towards traditional medications. The natural world is a rich source of medicines, with many drugs on the market originating from natural sources. As evolution progressed, these compounds developed to protect the organism against pathogens in the environment. Now, scientists are exploring the natural environment with renewed vigour in the hope of identifying new antibiotics.
Throughout sub-Saharan Africa, Hippopotamus amphibius inhabits environments such as rivers, lakes, and swamps, which provides shelter and cooling from the scorching hot sun. To adapt to these environments rife with microorganisms, the hippopotamus produces compounds (such as hipposudoric acid) that are secreted through its skin to protect itself from invading pathogens. The secretions are red in colour and change overtime to a brown colour and serve to protect the animals from their harsh environments. The molecular structure of the secretions has been elucidated & the colour change attributed to be the polymerisation of hipposudoric and norhipposudoric acids. These natural products exhibit absorbent properties towards dangerous UV rays and demonstrate antimicrobial activities. This discovery provides a chemophore with potential utility as therapeutic agents.
Yoko Saikawa et al. reported the synthesis of the natural products; however, their procedure featured many steps which causes the procedure to be time-intensive, expensive, & inefficient. This provides a clear opportunity to establish a novel synthetic route to produce the tricyclic scaffold of the natural product. This research is of great importance as it will enable the discovery of a new generation of antimicrobials.
To successfully develop a novel series of pharmacologically active natural products based on those from Hippopotamus amphibius for medical and commercial benefit. The proposed project is highly multi-disciplinary in nature.
First supervisor: Dr Stephen Wren firstname.lastname@example.org
Over the last decades, 2D or few-layer materials have been the subject of extensive research by chemists, physicists and material scientists. Due to their physical and chemical character (e.g. large surface area, unique electrochemical properties and unusual mechanical stability), 2D materials exhibit enormous potential for various types of applications ranging from electrode materials to bio/chemical sensors and biomedical research. Their electronic structure and reactivity can be tuned by modifying their surface (e.g. element doping) or the crystal lattice (introduction of vacancies), which allows the tuning of bandgaps and surface reactivity for catalytic applications. While there is a plethora of multi-element 2D materials (e.g. boron nitride or transition metal dichalcogenides), the number of mono-elemental 2D materials is limited, the most prominent example being graphene. However, the main disadvantage of graphene in electronics or as an electrocatalyst is its susceptibility to oxidation and the lack of a bandgap (not being a semiconductor).
This PhD project will focus on the design of innovative black phosphorus (BP) materials. BP is the most stable allotrope of phosphorus and crystallizes in a layered, puckered honeycomb structure. Theoretical studies predict that the bandgap of BP materials closely correlates with the number of layers. Therefore, few-layered black phosphorus (FLBP) exhibits enormous potential e.g. for optoelectronics and electrocatalysis. Due to its high carrier mobility, thickness-dependent direct bandgap and anisotropic physical properties, FLBP has also been considered for important applications such as sensors, batteries, photonics and transistors.
The successful candidate will gain a high level of inorganic synthetic skills (e.g. handling air-sensitive compounds, material processing, precursor synthesis). Besides the synthesis of bulk BP and exfoliation to produce FLBP, the student will work on the in-situ surface-functionalization of FLBP sheets. By modifying the surface of the BP flakes we will i) improve their stability towards oxygen and moisture, ii) create stable dispersions in aqueous and non-aqueous media iii) change their electronic properties (e.g. bandgap) and iv) create unprecedented (catalytic) surface reactivity. The lone pairs located on each P atom endow the layered BP structure with a unique (Lewis basic) reactivity and can serve as a functionalisation anchor. Electrophilic reagents can form Lewis pair donor-acceptor interactions that will bind them to the materials' surface and introduce functional groups. Furthermore, we will reduce metal salts (e.g. Bi, Au, Ag) in-situ on the BP surface to dope it with metal nanoparticles.
To gain a fundamental understanding of the new materials' properties, their thorough characterisation is very important. Thus, the student will be introduced to a wide range of analytical skills (e.g. XPS, XRD, SEM, TEM, AFM and Raman spectroscopy). Bandgaps will be probed using photoluminescence (PL) & NIR spectroscopy, and dispersion stability by dynamic light scattering (DLS) and Zeta potential measurements.
Finally, the student will investigate the catalytic potential of BP composites. For example, such "tuned" FLBP materials are potential catalysts for the transition-metal free photochemical or electrochemical activation of carbon dioxide to convert it into valuable chemical compounds, such as methane (CH4), formic acid (HCOOH) or methanol (H3COH). Together with a world-class collaborating group (University of Stockholm) we will develop catalytic membranes and photo-electrodes by thin-film deposition onto suitable substrates (such as zeolites or transparent glass-slides).
First supervisor: Dr Dominikus Heift email@example.com
The analysis of contaminants in the different environmental compartments requires extensive sample treatment methods to capture and purify trace levels of targeted substances in relatively complex media. Environmental monitoring is a growing need. Paradoxically, the analysis of pollution is done with analytical processes that cause important chemical waste. For environmental monitoring of a single site, hundreds of disposable plastic extraction cartridges, syringes and filters are typically used. Furthermore, these single-use consumables are costly and make environmental monitoring unaffordable for labs with low budget and where the analysis may be most needed.
The present work will prepare materials for analytical used based on natural products. The starting point will be natural sorbents with suitable characteristics. Going further, natural materials will be derivatised to improve their properties for their use in analysis. Finally, components from natural products will be used for the synthesis of advanced materials with optimal characteristics. The development in this thesis will be done following green chemistry practices. The target analytes will be perfluoroalkyl substances, bisphenols and other components of plastic materials. The PhD candidate will learn synthesis, characterisation of materials as well as relevant sample treatment techniques (e.g solid phase extraction, liquid phase microextraction) and analytical techniques such as LC-UV, GC-FID, GC-MS, LC-MS and SEM-EDX, XRD.
The project requires background in materials science, inorganic, organic, physical chemistry, and analytical chemistry and, importantly, interest in environmental topics.
First supervisor: Dr Rosa Busquets R.Busquets@kingston.ac.uk
Cocaine is the second most widely used illicit drug in the UK and the number of deaths involving cocaine was at an all-time high in 2015. Despite the risks and widespread use of cocaine, it's long term effects on organs such as the heart is still unclear. Studies on the effect of cocaine in hepatocytes have shown that cocaine may contribute to the pathogenesis of fibrosis, but the signalling mechanisms involved are still unknown. Benzoylecgonine (BZE) is considered one of the major metabolites following cocaine administration (Kolbrich et al., 2006).
One of the integral part of several cellular processes is actin rearrangement, such as motility (Etienne-Manneville and Hall, 2002), adhesion (Travis and Bowser, 1986) and cell division (Maddox and Burridge, 2003). Dysregulated actin rearrangement and the mechanisms by which it occurs are of significance not only in fibrotic diseases (Crean et al., 2004; Furlong et al., 2007), but also in conditions whereby cells exhibit uncontrolled growth like cancer (Volk, et al., 1984; Etienne-Manneville, 2008).
Fibrosis is defined as the uncontrolled and excessive deposition of extra-cellular matrix (ECM) proteins in response to injury and inflammation. The process of cell motility, actin rearrangement and wound healing are closely linked and regulated. This cell signalling network becomes uncoupled in fibrosis and leads to excess deposition of matrix proteins. The central event in cardiac fibrosis, is the activation of cardiac fibroblasts, a transformation from quiescent vitamin A-rich cells to proliferative, fibrogenic and contractile myofibroblasts. This process is underpinned by alterations of cellular polarity and cytoskeletal dynamics.
Our laboratory has recently demonstrated that cocaine induces significant changes in cell morphology and that higher concentrations induce cell death. Treatment of cardiac myocytes with cocaine significantly increased cell migration and adhesion, with no effect on cell proliferation. Higher cocaine dose treatments were associated with significant cardiomyocyte cell death and loss of cellular architecture. Our results further highlight the importance of cocaine in mediating cardiomyocyte function and cytotoxicity associated with the possible loss of intercellular contacts required to maintain normal cell viability, with implications for cardiotoxicity relating to hypertrophy and fibrogenesis (Verma et al., 2021).
The aim of this project is to further investigate the mechanism behind these physiological alterations and to determine whether BZE can act as a pro-fibrotic mediator in cardiac tissues.
First supervisor: Dr Elena Polycarpou E.firstname.lastname@example.org
The innate immune system can display characteristics of immunological memory. This phenomenon, termed "trained immunity", refers to the long-term functional reprogramming of innate immune cells after the encounter with infectious or non-infectious agents that influences their capacity to respond to a secondary stimulus. Many infectious stimuli, including bacterial or fungal cells and their components (LPS, β-glucan, chitin) are considered potent inducers of innate immune memory, enhancing the pro- inflammatory effects of the innate immune system. However, innate immune cells also arbitrate anti- inflammatory responses, therefore following exposure to appropriate cues they can be trained to be anti-inflammatory.
Research in the past decade has highlighted the broad benefits of pro-inflammatory trained immunity for host defence in the context of infectious disease; however, anti-inflammatory trained immunity could on the other hand have a protective influence against the development of immune-mediated diseases, of important therapeutic implications. Diseases mediated by a dysregulated immunity, such as inflammatory bowel disease, rheumatoid arthritis or asthma, are often treated with immunosuppressive drugs, which, although effective, are not voided of serious side effects. We have previously shown that immunomodulatory strategies that, instead of suppressing, promote the body's natural protective innate immune responses can effectively ameliorate disease progression in models of inflammatory bowel disease.
Innate immune memory may also play a role in the connection between early life exposure to microbes and patterns of disease susceptibility. Of note, epidemiological studies reveal a significantly lower incidence of immune mediated diseases in developing countries with a high prevalence of parasite infections. Thus, it is possible that parasites could induce an anti-inflammatory training program in our immune system that may be key in preventing the development of those conditions.
This project aims to examine the hypothesis that helminth-derived products can effectively induce a training program in macrophages, reprogramming them to be more anti-inflammatory in response to a secondary inflammatory stimulus. A secondary aim in the wider project will be to establish if anti- inflammatory trained immunity induced by helminth products confines a reduced susceptibility to inflammatory bowel disease.
First supervisor: Dr Nati Garrido Mesa N.Garridomesa@kingston.ac.uk
Neuronal injury or disease in humans often leads to disability and mortality. Neurons in humans regenerate poorly, and there are very few methods to encourage this regeneration. We are interested in identifying new genetic mechanisms involved in neuronal regeneration. Planaria are an excellent neurobiological model, have many neurotransmitters in common with humans, and have the remarkable ability to regenerate large parts of their nervous system after injury. This PhD project will undertake large-scale gene expression analysis in regenerating planaria head and eye tissue, followed by pharmacological and/or genetic inhibition of novel identified genes in regenerating planaria and cultured mammalian neurons. The objective is to identify novel regeneration-associated genes that may then be investigated further as potential target(s) for regenerative therapies in humans.
The student will gain experience in invertebrate model techniques, mammalian cell culture, RNA-seq and qRT-PCR analysis, protein analysis (Western blotting, immunofluoresence), molecular cloning, and microscopy. The student will join active and supportive research groups, and will be part of the Interdisciplinary Hub for the Study of Health and Age-related conditions (IhSHA) at Kingston University London.
Applicants should have or be expecting to obtain at least an upper second (2:1) class degree in a related subject (e.g. Biochemistry, Genetics, Biomedical Science). Experience in laboratory work or an MSc/MScR would be an advantage, but training on all techniques will be given.
First supervisor: Dr Fran Mackenzie F.Mackenzie@kingston.ac.uk
The two greatest challenges facing global healthcare are the ongoing Covid-19 pandemic and the insidious rise of anti-microbial resistant infections (AMR). To participate fully in the care of people with these infections, protect those at risk from infection and maintain personal safety at work, nurses need a good understanding of infection, its prevention and control measures (IPC). Infections can be a reason for hospital admission or may be acquired during a hospital stay, leading to longer hospital stays and more complications, both of which carry an additional financial burden for the NHS. While IPC is the remit of all staff, nurses are key to successful IPC implementation as they have the most contact with patients and their families, have a powerful role as patient advocate and are key drivers of ward culture. It has, however, regularly been observed that nurses' understanding of infection transmission and implementation of IPC measures on the ward is highly variable.
There is a significant gap in this research field as few studies address the experience of student nurses in terms of their IPC education and how that is translated into ward practice. This study will address that knowledge gap by investigating how student nurses are taught IPC across the UK, how confident they feel in their knowledge and application of it on the wards, and any opportunities or challenges they perceive in achieving IPC best practice.
This project would suit a candidate with clinical experience but a nursing qualification is not a pre-requisite.
First supervisor: Dr Suzy Moody email@example.com
Nasotracheal suction (NTS) is a procedure used to remove sputum in people who have difficulty with expelling sputum and coughing. NTS is an invasive method which requires inserting a catheter into person's airways and applying a negative pressure in order to remove sputum. It may be quite unpleasant or traumatic experience for the patient, it may cause adverse events, and it necessitates qualified healthcare staff.
The project is introducing a novel method by utilising nebulised capsaicin to avoid use the invasive NTS. Inhaled capsaicin is an irritant for cough reflex and it has been used for cough challenge testing but not as a therapeutic intervention. Capsaicin tablets will be formulated by direct compression. The physical properties of the prepared tablets will be assessed in order to guarantee suitable mechanical characteristics and pharmaceutical performance. The tablet will be designed to dissolve rapidly to prepare a nebuliser solution and release capsaicin to stimulate cough reflex which will lead to the expelling of sputum.
The designed tablets will undergo testing by healthcare staff in a case series with healthy volunteers, to determine usability and acceptability of the formulation and its application to healthcare staff.
Finally, a thorough Patient and Public Involvement (PPI) activity will investigate the views and experiences of patients who need NTS as wells as their family members and carers, through interviews and focus groups. The PPI activity will investigate the use of nebulised capsaicin as an alternative to NTS, and aspects of design and conduct of a clinical follow-on study.
First supervisor: Dr Amr Elshaer A.Elshaer@kingston.ac.uk
Quantum dots (QDs) are photoluminescent nanoparticles which reside at the cutting edge of opto-electronic device development. QDs owe their remarkable photoluminescence to their small size and semiconducting properties, with each of their dimensions smaller than the Bohr radius of the parent material in a strong confinement regime.
Research efforts have focussed primarily on improving optical properties, reducing "blinking" and toxicity of QD materials and improving biocompatibility. To date however, there have been very few reports of using QDs to catalyse organic transformations. In this role, QDs offer significant advantages, as they are light rather than heat-activated and straddle the gap between homogeneous and heterogeneous catalysis, meaning they are fully dispersed in the reaction solvent and easily isolable by centrifugation.
QDs are often highly susceptible to oxidation, as well as being synthesised of toxic materials. The overgrowth of shell materials with similar lattice parameters and lower toxicity can preserve/enhance QD photoluminescence, prevent oxidation and inhibit leaching of toxic ions. For typical II-VI and III-V core materials (such as CdSe and InP respectively), ZnS is an excellent non-toxic, wide band-gap shell material.
Early synthetic methods for ZnS shells focussed on the use of highly pyrophoric alkyl zinc and malodorous silathianes. Whilst successful, these reagents require careful handling and specialist equipment (e.g. nitrogen gloveboxes) to use effectively. Since the early 2000s, alternative routes to QD shells based on the decomposition of air-stable, single-source molecular precursors emerged. In particular, inexpensive metal dithiocarbamate species which decompose cleanly into ZnS (and volatile organics) at low temperatures have moved to the fore.
In 2014, Bear, Hogarth et al. reported a method to synthesise composite QD shells on CdSe QD cores for catalytic applications. Our work showed that doping copper with zinc (synthesising a CdSe/ZnS-CuS core/shell QD) had a detrimental effect on the overall photoluminescence, introducing a second, long-lived photoluminescence feature, but was essential for catalytic activity. Catalytic activity was assessed using the "Click" reaction of phenylacetylene and benzyl azide under 254 nm irradiation, achieving ≥99% yield over several cycles for both the 1:1 and 1:3 molar ratio of copper:zinc. To date, this is unmatched in studies where QD catalysis and the same reaction was utilised. It was found that copper was released into solution by ICP-OES, and therefore postulated that the QDs act as catalyst vectors rather than true catalysts, albeit with impressive turn over numbers and able to catalyse multiple reaction cycles.
We will expand this work by synthesising new metal-sulphide single-source precursors for different core/shell systems. Work will focus on adding metals to CdSe/ZnS system, and expand the number of organic reactions catalysed, looking at carbonylation and carbon-carbon bond formation. In doing this, we will be able to ascertain how well the ZnS lattice reacts to doping with different metals, which will allow investigation of the mechanism of catalysis, which is currently unexplored. Subsequent research will focus on other QD core materials such as InP and carbon dots.
First Supervisor: Dr Joseph Bear J.firstname.lastname@example.org
The group has published on the discovery of heterocyclic quinone anti-cancer agents with specificity towards hypoxic cells associated with solid tumours, reductase enzymes over-expressed in cancer, and mutations in the FANC DNA repair genes.[3,4] We have developed safer nitric oxide (NO) donating vasodilators, that release NO up to 7 times faster than the commercial drug. Our passion is the synthesis of new heterocycles, which account for more than 80% of all pharmaceuticals, in particular quinones reductively activated in solid tumour cells.[6-8] Recently, we developed heterocyclic quinone prodrugs activated by visible light, which offer potential new photodynamic therapy treatments.[9,10]
Inherited mutations in certain genes, notably BRCA1, BRCA2 and FANC significantly increase susceptibility to breast, ovarian, prostate, and other cancers. The overall frequency of BRCA germline mutations is significant in the Arab population. The term "BRCAness" however applies more widely, as it includes non-inherited cancer cases for describing alterations in these genes and related proteins. The BRCA and FANC pathways play a key role in DNA damage repair and are therefore important in the response of cancer cells to chemotherapeutic (cytotoxic) agents. This research involves identifying molecular functionalities responsible for the BRCA/FANC pharmacogenomic response pathway and using such information to match patients with molecularly targeted therapies, so leading to the development of precision medicines. Structure-activity relationships from reported anti-cancer scaffolds (Figure 1) allow us to devise new synthetic targets. The project involves working in a multi-disciplinary team of chemists, cancer research scientists and clinicians in the UK and Qatar.
First supervisor: Professor Fawaz Al-dabbagh F.Aldabbagh@kingston.ac.uk
Nowadays in electric cars batteries contribute a large part of the vehicles' weight, which means additional foot printing and load-bearing function. From the other hand an integrated structural battery and supercapacitor system, is one that works with a double function as the power source and as part of the structure a car/drone/plane body. This is a "massless" energy storage, because in essence the battery's weight vanishes when it becomes part of the load-bearing structure. Calculations show that this type of multifunctional battery could greatly reduce the weight of an electric vehicle or other mobile application significantly. Recent development of structural batteries at Chalmers University of Technology has proceeded through many years of research, including previous discoveries involving certain types of carbon fibre. This work was named by Physics World as one of 2018's ten biggest scientific breakthroughs in our times.
Different approaches to improve battery run-time with supercapacitors has been done in the past decade and a series of papers was presented. Looking ahead at consumer behaviour it is conceivable that the next generation of electric cars, electric planes and satellites and intelligent houses will be powered by such structural composite systems.
The aim of this project is to generate different composites parts able to act like the power source battery function and have a powered electrical contribution.
First supervisor: Dr Spiros Koutsonas S.Koutsonas@kingston.ac.uk
Commercial aviation currently accounts for approximately 2.6% of annual global carbon dioxide (CO2) emissions from fossil fuel combustion. With the depletion and pollution of fossil energy, the demand for environmentally friendly power technology and efficient and clean energy for aviation has increased steeply in recent years. Proton exchange membrane (PEM) fuel cells are receiving increasing attention due to their good conversion efficiency, environmental characteristics, simple structure and low noise.
One of the most important and effective elements in the improvement of efficiency and power density of fuel cells are the bipolar plates. These components supply fuel and oxidants, remove generated water, collect produced current and provide mechanical support for the brittle membrane electrode assembly in fuel cell stack. PEM fuel cell performance is directly related to the bipolar plate design and their channels pattern. Power enhancements can be achieved by optimal design of the type, size, or patterns of the channels. The proposed project concentrates on improvements in the fuel cell performance for Aviation application by optimisation of flow-field design and channels configurations through numerical simulation, which requires power density robustness, compactness etc.
This project will require a sound understanding of aerospace propulsion system, computational technologies (CFD), as well as good modelling skills. It would particularly suit a graduate in Aerospace Engineering, Mechanical Engineering, or an equivalent area. Detailed background knowledge on fuel cell is not required, but enthusiasm, flexibility and motivation to success are essential.
First supervisor: Dr Yujing Lin Y.Lin@kingston.ac.uk
Electric Propulsion (EP) systems, used to manoeuvre satellites in space, have become commonplace in recent years, with many technologies creating plasma interactions with electromagnetic fields to expel mass at high velocities to propel the spacecraft. The increased specific impulse of EP systems over chemical propulsion systems can lead to enhanced overall net gains from a system perspective in given mission scenarios, making EP an attractive solution. A significant ongoing challenge when developing EP systems is the simulation, analysis, and qualification cycle that EP systems go through during their product development.
New EP systems can take years to develop due to the difficulty in determining and defining their performance and operation through simulation and physical testing. Simulation campaigns for some EP developments can take many months or even years to perform based on the complexity of the simulation model and the required accuracy of the simulated plasma process. Most models share a common feature in which they apply the Lorentz force equation and solve Maxwell's equations to evolve a system in which plasma is present. Several methods exist for simulating different types of thrusters such as fully kinetic particle-in-cell (PIC) models, hybrid PIC models and fully fluid models. The fully kinetic model tends to have the best fidelity but requires significant computational power to be of use in 3D space. Fully fluid solvers are used for rapid and efficient plasma modelling over longer timescales but suffer from limitations such as an inability to model non-Maxwellian behaviour. Even then generalised models are not always able to simulate electric propulsion systems fully, due to some of the EP technological complexities, and so it is necessary for custom specific models to be created. Even if these are not important factors the computational resources for large scale models are not always available.
To solve many if not all these issues, a Graph Neural Network model dubbed PlasmaNet has been conceptually developed and through this studentship will be developed further. PlasmaNet takes inspiration from work carried out with Graph Nets for physics simulations by DeepMind. The model proposed will have three main components, the Encoder, the Processor and the Decoder. The encoder and decoder will be Multilayer Perceptron Neural Networks (MLPNN) and will compress the inputs into a latent space representation which will be passed into the processor and then decompress the low dimensional output of the processor back to high dimensional data respectively. At the current proposed state, the processor (graph net) will be performing node and edge predictions where these will be analogous to the change in a particle's parameters and its relationship with other particles. The edge for each neuron will be drawn using the Debye length of the plasma. The graph will be (re)drawn with each new loop of the system.
However, one fundamental principle of plasma physics is not accounted for in this concept, species annihilation and creation. It is suggested that a traditional algorithm can be used for this after the predicted data is decoded but will likely be an inefficient methodology especially when many particles are simulated in turn not allowing for the full potential of using neural networks for this type of simulation. The aim of this study will be to investigate a way in which graph level predictions can be made where the goal is to design an algorithm to annihilate and spawn neurons dynamically.
This study will mainly focus machine learning and neural network where the plasma physics understanding required is minimal and can be learnt during the study. Simulation data from UCLA's OSIRIS particle-in-cell (PIC) plasma simulation tool will be used to train the model. The successful completion of this project will see the underpinning principles application to a variety of fields aside from plasma physics.
The project would suit a graduate in Engineering, Physics or Applied Mathematics with interests in Computing. It is desirable for applicants to be familiar with Python, Fortran or C, machine learning (especially neural networks), Linux and high-performance computing.
First supervisor: Dr Peter Shaw P.Shaw@kingston.ac.uk
There have been consistent efforts in developing more fast and efficient combustion. Microwave discharges are widely used for plasma-assisted ignition of air/fuel mixtures. Among the various types of discharges studied, the microwave discharge has recently demonstrated promising characteristics for ignition at low initial temperatures. Possibilities of the use of a sub-critical streamer discharge to ignite air/fuel mixture are analysed numerically. The effect of ignition area on the propagation of a premixed flame in subsonic and supersonic flows is obtained with the numerical models. The results of numerical simulations are compared with the experimentally measured quantities.
The successful ignition of a combustible mixture not only initiates the combustion but also influences the subsequent combustion process. The ignition system has always posed problems in commercial applications. Many experimental, theoretical and numerical studies have been performed for the past years and various ignition methods (e.g., electric discharge, corona discharge, radio-frequency resonant discharge, microwave discharge, laser radiation) have been tested to achieve simultaneous space ignition via multiple ignition points throughout the combustion chamber to establish a high-performance ignition method.
Plasma-assisted combustion is a promising technique to improve engine efficiency, reduce emissions and enhance fuel reforming. Ignition and combustion control using cold and non-thermal plasmas appearing in microwave discharges has become a major topic of interest. Microwave discharges are widely used for generation of quasi-equilibrium and non-equilibrium plasma. Among the various types of discharges studied, the microwave streamer discharge has recently demonstrated promising characteristics for ignition at low initial temperatures. The streamer discharge looks as a chaotic structure of plasma channels (filaments). Their characteristic diameter is about a fraction of millimetre, and a characteristic distance between the channels is a fraction of wavelength. A streamer filament divides itself into several branches that connect to each other forming a net of thin plasma filaments, whose characteristic length is related to electro-dynamic resonance effects. A local initiation of such a discharge is provided by special facilities.
Streamers understanding involves many scales from the microscopic scale of collisions of electrons with neutral molecules to macroscopic scales ranging from thin space charge layers within each streamer finger up to the streamer tree with possibly thousands of branches. One of the main properties of a developed streamer discharge is that it absorbs almost all the electromagnetic energy incident on it. This is a significant difference between streamer discharges and spatially uniform discharges. Another important feature of streamer discharge is that the streamer spreads in a considerable volume, and the strength of electrical field is much smaller than the critical one (sub-critical microwave discharge).
In this project, possibilities of the use of microwave radiation to initiate combustion of air/fuel mixtures are investigated. The streamer discharge is formed by a electromagnetic beam with the electrical field strength which is smaller than the minimum pulse intensity leading to the air breakdown. The discharge is ignited in a space far away elements forming electromagnetic beam. Premixed flame propagation of air/propane mixture is investigated through a numerical simulation using Navier-Stokes equations coupled with chemical reactions and Maxwell equations. The results of computational studies of combustion of air/propane mixture are compared with the available experimental data. A major cause of the flame acceleration related to an increase in the flame surface area and thereby the total heat release rate, is one of the potential methods in improving combustion efficiency by reducing the burning time in the propulsion systems.
First supervisor: Dr Konstantin Volkov email@example.com
In many cities, the existing buried transport infrastructure can be of significant age. At the time of its construction, accessibility, particularly for those considered as Mobility Impaired Persons (MIPs), was not considered. The result of modern disability legislation combined with the mobility needs of an ageing population has increased the urgency for this neglected issue to be addressed and one viable solution is to install lifts down to platform level. This necessitates the construction of a deep shaft but a fundamental issue facing engineers is that such a shaft would generally need to be constructed within the vicinity of aged, buried infrastructure. Any underground construction activity has the potential to cause damage to elements of existing infrastructure because of the additional loads or movements generated. This is because the stiffness and strength characteristics of the ground between elements of buried infrastructure are often significantly degraded by new nearby underground construction. Owners of these assets therefore place onerous constraints on developers. Engineers, working on behalf of developers, inevitably specify overly conservative designs for new buried infrastructure because there is a lack of detailed understanding of the interaction between new and existing buried infrastructure (Chudleigh et al., 1999). There is a requirement, therefore, for there to be a greater understanding of the soil-structure interaction that takes place during and following the construction of new infrastructure. That greater understanding would lead to more confidence in design specifications and reduce the risk associated with new underground construction. This has wider societal impacts which will allow for greater accessibility because of the improved pedestrian flow through underground spaces for MIPs.
An essential stage in any geotechnical design is an assessment of ground movements resulting from construction and their effects on existing infrastructure. Any underground construction will cause ground movements which have the potential to damage existing infrastructure. Various investigations have been conducted into the overall behaviour and stability of shafts (e.g. Britto & Kusakabe, 1982; Morrison et al., 2004) including Faustin et al. (2018) who recently reported 27 case studies of circular shaft construction in London. Whilst none of these studies made specific reference to the effect of varying the cross-sectional shape of a shaft, recent experimental work (e.g. Le et al., 2019) has begun to address this issue.
Sinking a circular shaft has many advantages in terms of relatively simple structural design and radially symmetric movements however the size required to house lifts (which are generally rectangular in shape) might be very large and in a congested urban environment (i.e. where there are already foundations, services and other structures below the ground surface) this might not be practical. As well as being an option enabling shafts to be sunk between existing structures, there are efficiencies to be made in terms of volume of material excavated (and thus to be disposed of) by considering non-circular shaft cross-sections. This project will use a validated finite element model to investigate the movements generated both at and below the surface during construction of non-circular deep shafts in stiff clay. The model will be validated against previously published case studies and experimental work. The project may also investigate how this movements affect existing infrastructure (e.g. a tunnel or station box) in terms of addition strains induced and how they may lead to structural damage.
First supervisor: Dr Richard Goodey R.Goodey@kingston.ac.uk
In supporting the UK Government to fulfil its targets on carbon emissions and environmental protection, there is an increasingly strong emphasis on recycling and reuse of construction waste in civil engineering. In this study, a sustainable capillary barrier cover system incorporating Recycled Concrete Aggregates (RCA) is proposed to be developed. Fine recycled concrete aggregates (FRC) and Coarse Recycled Aggregates (CRC) will be used to form the fine-grained and coarse-grained soil layers of a two-layer capillary barrier (CCBE). According to previous studies, the permeability of compacted RCA decreases while the strength increases over time due to the self-cementing properties of compacted RCA.
In this study, the primary objective is to investigate the key hydro-mechanical mechanisms that influence single compacted RCA layers and hence assess the performance of a sustainable RCA based capillary barrier cover system. A range of laboratory tests, including 1D soil column tests and 2D flume tests, will be carried out along with numerical simulation and field testing to monitor the long-term performance of the sustainable cover system.
The expected deliverable from this study is an advanced hydro-mechanical model which can be used to analyse compacted RCA elements taking into consideration: (i) the self-cementing characteristics of RCA, (ii) the mechanisms of water infiltration in a RCA based sustainable capillary barrier cover system and (iii) optimum combinations of soil materials and layer thickness for the barrier system under various climate conditions.
A successful outcome of this work is expected to lead to improved design methods for barrier systems for structural foundations and landfill structures subjected to dynamic loading and seismic effects.
First supervisor: Dr Hsein Kew firstname.lastname@example.org
This project investigates the influence of the incorporation of shear thickening fluid (STF) on the impact behaviours of composite and Fiber Metal Laminate (FML) panels. The unmodified and modified composite specimens and FML panels with different configurations will be fabricated using a hand lay-up or other method and will be investigated through high-speed impact testing. The tests will be conducted with a flat-ended projectile and the loading conditions will be the same for all samples, except support spans which will be varied. Following experimental testing, damages at the punch region will be extensively investigated, and localized and global damages will be monitored. In the theoretical part of the project, an analytical model for the high-velocity impact behaviour of these kinds of panels under flat-ended cylindrical projectile will be presented. This model employs an energy balance approach to derive the governing differential equations related to the process. The model will consider all mechanisms including crushing, tensile fracture, plug kinetic, and friction loss. A closed-form solution will be presented to the derived governing differential equations using singularity functions. Singularity functions will be used to incorporate discontinuities due to various energy-absorbing mechanisms. The results of the model will be compared with the presented experimental results. The effects of various impact parameters on the residual velocity and energy absorption will be studied and the results will be reported, discussed, and commented upon.
First supervisor: Dr Samireh Vahid S.Vahid@kingston.ac.uk
Composites are, besides aluminium, the most important materials for aerospace applications. Particularly, the introduction of carbon fibre reinforced polymeric resin (CFRPs) subsequently opened a door into composite research and innovation, which theoretically promised further weight savings with a wide range of physical properties and an ability to tailor the materials for specific purposes. Although composites provide a great deal of advantages and enhanced characteristics which have driven improvements to their structural and aesthetic applications, there still lie major disadvantages and drawbacks in the recycling of these life limited materials as well as the ecological harm in utilising the petroleum-based resins. Hence, the purpose of this research is to fabricate bio-based composite material for aircraft interior panels and non-load bearing structures which can be fully biodegraded at the end of its service life, and further will work towards reducing the waste production of the composite material industry.
There are a number of naturally available fibres and bio-based biodegradable composites. Hence, using these bio-based fibres will yield to a 100% recyclable composite. Since the aerospace industry is huge, making of internal panels using these biodegradable composites will be of huge interest. This not only helps in energy saving but also reduce considerable the carbon emission. Hence it is not only a national interest but also global interest.
The purpose of this research is to fabricate bio-based composite material for aircraft interior panels and non-load bearing structures which can be fully biodegraded at the end of its service life, which will work towards reducing the waste production of the composite material industry.
Although composites provide a great deal of advantages and enhanced characteristics which have driven improvements to their structural and aesthetic applications, there still lie major disadvantages and drawbacks in the recycling of these life limited materials as well as the ecological harm in utilising the petroleum-based resins.
The purpose of this research is to fabricate bio-based composite material for aircraft interior panels and non-load bearing structures which can be fully biodegraded at the end of its service life, which will work towards reducing the waste production of the composite material industry.
In this study, in-depth research will be carried out on natural fibre reinforced composites (NFRCs). The product of this research resulted in the fabrication of a bio-based biodegradable composite. Furthermore, the feasibility of utilising the bio-based biodegradable composites in the internal panels of the aircraft structures will be studied.
First supervisor: Dr Doni Daniel D.Daniel@kingston.ac.uk
The Vaiont landslide occurred in 1963 in northern Italy and has perplexed researchers ever since. Recently, however, new geological information and interpretations have led to an entirely new framework for understanding the event. We are now in a position to investigate the detailed controls on the occurrence of the landslide and to reliably assess the associated uncertainties.
This project will utilise 2D (and possibly also 3D) stability analyses to establish the sensitivity of controlling parameters, the relative influences of groundwater and external reservoir levels (including the true Critical Pool level), and the effects of data resolution, on the calculated stability of the landslide. These results will enable the determination of the most likely overall slope conditions that gave rise to the failure.
A further critical element of this project is to analyse the rainfall in terms of long-term records for the region in order to quantify the statistical significance of the rainfall during 1960-63 and, thus, estimate the magnitudes of groundwater responses within the mountain slope compared with likely long-term variations.
First supervisor: Dr Alan Dykes email@example.com
There is an increasing urgency to understand the biochemical and physiological responses of plants to global environmental change. This is because environmental stressors––climate and land-use change, environmental pollution (e.g., nitrogen deposition) experience in the vicinity of urban areas––directly influence species' survival, growth and reproduction, which in turn determine community structure and function, as well as ecosystem processes. In effect, environmental stressors drive permanent, long-term changes in ecosystems. Equally important, plant functional traits can be altered as plants adapt (acclimate) to environmental constraints, and therefore, variation in plant responses and tolerances to environmental stressors can be captured by directly measuring fluctuation in the most relevant functional traits. However, retrieving rapid, reliable, and repeatable measurement of key plant traits indicative of plant stress has proven challenging. It is within this framework that we will investigate climate-driven environmental stressors using hyperspectral (HS) leaf reflectance to better understand plant responses to drivers of environmental change.
The project will focus on observations collected near urban and peri-urban landscapes, where ecosystems are continuously exposed to multiple environmental stressors, including climatic abnormalities (e.g., high sunlight radiation, extreme temperatures, highly variable water inputs) and high levels of ozone and other air pollutants (Tausz et al. 2007, Bussotti 2008, Paoletti et al. 2010, Sharma et al. 2012). These environmental conditions can induce plant oxidative stress because of increased production of reactive oxygen in plant cells. Plants exhibit species-specific tolerances against such oxidative injury, which is modulated by their efficiency at mobilizing antioxidant defences (i.e., balancing pro-oxidant and antioxidant levels). Recent studies have established that both Non-enzymatic (e.g., carotenoids, ascorbic acid and glutathione) and enzymatic substances (e.g., superoxide dismutase, catalase, ascorbate peroxidase and glutathione reductase) are important antioxidants, and can therefore be used to characterise plant susceptibility to natural and anthropogenic oxidative stress (Iriti and Faoro 2008, Foyer and Noctor 2011, Foyer and Shigeoka 2011). Additionally, other leaf compounds, such as the levels of chlorophyll or hydroperoxide, may also help characterise plant susceptibility to oxidative reactions (Gratao et al. 2012). Equally important, plant functional traits can be altered as plants acclimate to environmental constraints and their responses to environmental stressors can be captured by directly measuring fluctuation in plant functional traits, such as biochemical leaf traits. This project will investigate species-level physiological tolerances to environmental stressors and up-scale to community -and- ecosystem-scales. We will focus on biochemical leaf and root traits that are ideal for measuring plant tolerance to (against) environmental stressors. The project involves multidisciplinary work including acquisition of HS data, exploration of the spatial distribution of the environmental stressors in relation to urban areas, and application of artificial intelligence and spectral imaging techniques to create predictive models.
First supervisor: Dr Olga Duran O.Duran@kingston.ac.uk
Throughout the history of nature conservation there have been major conflicts of interest between different stakeholders, for instance, between national governments and local communities. These conflicts have been most profound and visible for international conservation interventions, such as establishing protected areas in lesser-developed countries. However, with the recent focus on implementing nature-based solutions (NbS) in urban and peri-urban landscapes, the fraught nature of conservation interventions is increasingly being felt in urban areas. Certain stakeholders perceive NbS as ‘messy', unkempt, and unmanaged spaces, while local authorities view them as vital for mitigating effects of anthropogenic climate change (ACC) in urban and peri-urban areas, (e.g., planting urban forests, creating urban green -and- blue-spaces and re-wilding initiatives). NbS projects are thought to maximise delivery of ecosystem services, improving everything in urban landscapes ranging from health and well-being and carbon sequestration to biodiversity and flood mitigation. The benefits of NbS are seen by certain stakeholders as an acceptable solution to attenuate the negative impacts of urbanisation on natural habitats by safeguarding some of the benefits of nature in urban spaces. However, NBS projects are far-removed from traditional manicured and managed urban landscapes, wherein urban gardens and forests, or re-wilding zones, come into direct conflict with citizens' preferences and perception of what is acceptable in their neighbourhood.
It is within this framework that the project will contribute to address a fundamental knowledge gap: what factors shape citizens' perceptions, attitudes, and behaviour toward NbS in urban and peri-urban environments? The primary aim of the project will be to investigate barriers to implementing nature-based solutions for climate adaptation in urban and peri-urban environments. A complementary aim is to investigate whether ecosystem services provisioned by NbS are aligned with people's perception of the NbS. The programme of research will take an interdisciplinary approach, integrating social science (e.g., contingency choice experiments) with ecology (e.g., biodiversity and ecosystem function) to get a comprehensive understanding of climate adaptation and mitigation policy adapted vis-a-vis NbS initiatives..
First supervisor: Dr Kerry Brown K.Brown@kingston.ac.uk
The proclamation of the Montreal protocol in 1987, phasing out ozone depleting halons as fire suppression agent has led to a worldwide research and development effort to find alternative systems. Water has clearly emerged as a potential replacement method to halons, mainly for its non-toxicity and low cost. Water sprinkler and water mist systems are widely used nowadays in fire protection. In a typical water mist the average droplet size is about 100 microns (few millimetres for sprinklers).
Past research studies have overlooked the potential of gas-like fine mist, called micromist (droplet size typically about 20 microns) for fire suppression. However, in recent years, there has been a worldwide renewed interest in micromist water systems for fire suppression for the following main reasons
The main goal and novelty of the proposed PhD project is to investigate the fire suppression capabilities of fine micromist generated by flashing of superheated water through both experimental and theoretical methods. The key objectives are:
First supervisor: Dr Siaka Dembele S.Dembele@kingston.ac.uk
Aircraft research is in a state of transition, with a much broader range of possible new aircraft technologies under investigation than ever before. New technologies include boundary layer ingestion, hydrogen fuelled aircraft, electrically powered aircraft and distributed propulsion schemes [IATA Technology Roadmap to 2050 (2019)]. Most of these are driven by the imperative to reduce carbon emissions in order to reduce, and indeed reverse, the growth of the carbon footprint produced by the increasing number of air miles flown each year [IATA 20 year passenger forecast (2019)].
This imperative has reinvigorated the notion of the use of ballonets to produce lift in aircraft, primarily in assisting craft to reach their cruising altitude, and considerable research interest in this area has been generated [Zhu et al (2021), Wang et al (2021)]. It is estimated that up to 20% of the total fuel consumption for a short-haul flight is consumed in reaching the required cruising altitude alone [see, for instance, the Quora reference below], and at the moment the energy content of the fuel is completely lost. This project proposes to investigate the use of ballonets to assist in fuel reduction by production of buoyant lift, by modelling a range of possible configurations and the use of a mix of potential gases. A number of possible configurations will be investigated, such as the use of fixed or inflatable elements, along with potential energy recovery in descent by re-pressurisation of the buoyant gases.
This project will require a sound understanding of physical principles, especially those relating to flight and buoyancy, as well as good modelling skills, It would particularly suit a graduate in Physics, Aerospace or Aeronautical Engineering, or an equivalent area. Detailed background knowledge in this area is not required, but enthusiasm, flexibility and a drive to make progress are essential.
First supervisor: Professor Andy Augousti firstname.lastname@example.org
Development of autonomous vehicles is seeing a growth in many different applications. As we increase the levels of automation and move into self-driving cars, it is expected that these systems will combine a variety of sensors to perceive their surroundings in a robust manner and avoid human errors. They also need to adapt quick to changes in their environments and make decisions based on a number on inputs that might be contradictory. Moreover, reports have found that autonomous vehicles can be vulnerable to a wide range of attacks such as physical perturbations or back-end malicious activity.
This project looks at AI robust perception methods that combine a range of sensory inputs including computer vision to identify changes and suspicious sensor responses in order to make robust decisions on motion and planning. The project will also look at terrain models and vehicle- terrain interaction using specialised sensors such as hyperspectral cameras. Terrain map requires numerous on-the-ground observations and sample collection. The use of intelligent models obtained via hyperspectral sensors combined with artificial intelligence provides an alternative technique to traditional mapping that can derive not only quantified terrain information but also texture and manoeuvre decisions. The project combines, sensors, hyperspectral imaging, signal and image processing, artificial intelligence and control.
First supervisor: Dr Redha Benhadj-Djilali R.Benhadj-Djilali@kingston.ac.uk
The importance of space and time have been evident in news briefings, media coverage, statistical reporting and public commentary throughout the COVID-19 pandemic. This has elicited responses ranging from ‘we are all in this together' through to ‘let us do what we want' because cases and deaths are less in our locality. This PhD project investigates the patterns of local spatial diffusion of the COVID-19 pandemic and aims to understand the spatial inequalities in the number of COVID cases and deaths in different parts of UK. In doing so, it aims to identify the associated covariates that may affect its spread, as well as the control measures and the rate of vaccination in each area at the time of concentrated outbreaks.
The number of new COVID cases and related deaths have been reported daily at small enumeration units, yet it is often their aggregate figure across UK, along with the R value (the reproduction number) and the growth rate of COVID, that have been used for informing political decisions such as lockdowns, self-isolation policies and other measures to control the spread of the disease. They are often quoted without accounting for the unevenness of the spread and concentration of COVID, which would benefit from a more targeted control in high-risk areas. There are also associated covariates identified by the Office for National Statistics (ONS) and other entities, and these include ethnicity, age groups, population density and deprivation. However, their impact on the spatially uneven spread of COVID is also understudied. Adding to the complexity is the progress of the 1st, 2nd and 3rd/booster vaccinations which is different in each area and by age groups and may be affected by the prevalence of local anti-vaccination movements; yet their effect is often measured by the overall national rate of vaccinated population.
The project seeks deeper understanding on what kind of changes have taken place, how they have affected the outcomes. This in turn gives us a clearer idea to explain the spatial diffusion of COVID and design a forecast model to estimate when and where it will happen under what conditions. Imminent release of data from the 2021 Population Census will allow the incorporation of spatially-temporally dynamic variables as covariates and control measures and their analysis across space and time.
The model will be designed to assess the impact of changes in the space-time variables and forecast the likely outcome of the spread of COVID. It will account for the covariates on the state of COVID cases and the temporal latent structure of the additional space-time variables on the outcomes with a scope to evaluate the influence of policy changes on control measures, and what can be done to improve the likely outcomes.
First supervisor: Dr Naru Shiode N.Shiode@kingston.ac.uk
There has been strong interest in applying innovative morphing wings to the next generation passenger-carrying jets, eco-friendly electric aircraft and drones to improve aerodynamic efficiency, aligning with the sustainability development in future aviation. Morphing wings were inspired initially by biomimicry through studies on the flapping motions of birds' wings. The shape of the wing can be variable and optimised for maximum flight performance at different flight operating envelopes. Numerous research studies have been conducted in the materials, structures, aeroelasticity and aerodynamics of morphing wings (Ozel et al. (2020), Khac et al. (2010), and Sun et al. (2016)). The majority of the existing research focused on both unsteady and steady aerodynamic characteristics and drag reduction using two-dimensional CFD simulations such as the works of Bashir et al. (2021), Kan et al. (2020) and Abdessemed et al. (2019). They used the NACA 0012 aerofoil because extensive numerical simulation and experimental data were published.
This study investigates the steady-state characteristics of both wings with Leading-Edge (TL)and Trailing-Edge (TE) morphing using the NACA 0012 at the same conditions in recent computational studies. The originality and added value of the current proposal are that we will evaluate the combined effects of variable leading edge and trailing edge together, as most previous studies focused primarily on morphing trailing edge alone. In addition, the effects of the length of the LE morphing "slat"and TE morphing "flap" will be investigated in the parametric study. Currently, there is a gap in how we can effectively implement the fundamental research results of simple symmetrical aerofoils to real-world applications in electric aircraft and unmanned drones.
The methodology involves the application of ANSYS Fluent using different turbulence/transition models for the CFD flow simulations. XFOIL software will be used as the preliminary design tool, and the results will be validated against the RANS simulations using Fluent. A morphing deformation algorithm using MATHLAB will be developed to transform a NACA 0012 aerofoil using a polynomial function for the camber. The parametrisation of the geometry includes variations in LE and TE deformations and the length of the deformation region.
First supervisor: Dr Sing Lo S.Lo@kingston.ac.uk
In recent years occurrence of antibiotics in aquatic systems has attracted significant attention, particularly in rivers which receive sewage treatment effluent where they have been found to bioaccumulate in aquatic organisms, albeit in trace amounts. Antibiotics enter wastewater streams, mainly through their usage/excretion and inappropriate disposal and undergo degradation during wastewater treatment.
Existing data on pharmaceuticals/antibiotics removal/degradation in wastewater treatment is limited and show highly variable removal efficiencies, possibly due to the difference in technologies and operating conditions used in wastewater treatment plants (WWTPs). Clearly, there is lack of understanding the link between antibiotics usage and their environmental impact in receiving waters following sewage effluent discharges. This is important to understand, particularly the extent to which antibiotics are degraded/removed during wastewater treatment. Understanding the link between antibiotics usage and the extent of their degradation in WWTPs is essential to develop more effective wastewater treatment strategies/technologies to mitigate their environmental impact in receiving waters.
This project seeks to investigate antibiotics usage, their removal in wastewater treatment and environmental impact by determining their concentrations in sewage influents, effluents receiving waters, with an aim of modelling the entire process (usage, degradation/removal and environmental impact). Findings from this research are likely to be useful in designing effective wastewater treatment strategies/technologies to mitigate antibiotics' environmental impact in receiving waters. The work will target three-to-four contrasting capacity WWTPs and will consider seasonality (e.g., greater use of antibiotics and high river flows in winters).
The project requires a strong background in organic chemistry and organic chemical analysis.
First supervisor: Dr Peter Hooda P.Hooda@kingston.ac.uk