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The Art of Justice: Reconfiguring the Courtroom Object
Get PDFPh. D. ThesisThis research intervenes in the material culture of the courthouse to establish
new rituals that inform public understandings of the law. Art installations
installed in the courtroom critique the symbolic materiality of law’s historical
artifacts. The creation of objects, and their roles in new embodied courtroom
performativities, challenge existing courthouse rituals and expose the need for
new ones to convey revised messages to the public. The courtroom object at
the centre of my research is the Admiralty’s silver oar. It has its origins in the
earliest admiralty court, during the reign of King Edward III in the 1360s. It was
the only courtroom object processed to the gallows and it is still processed and
displayed in courtrooms in the UK and globally today.
The PhD extends to the courthouse environment. Data gathered on courtroom
acoustics revealed how the architecture and acoustics of the historic court
silenced, or facilitated, those involved in judiciary processes. These datasets,
along with visualisations of the sound movement within the space and archival
research, were employed as a source for producing site-specific artwork. My
work also examines representation and responsibility in contemporary public art
in the courthouse and the woman’s voice in historic sites of law and order. My
PhD is cross-disciplinary, drawing on methods and approaches from Fine Art,
Art History, and History. There is a ‘moral value’ approach to some western
public courthouse commissions by artists and commissioners and evidence of a
tendency for artists working site specifically in the courthouse to refer to
established symbols of justice that are still widely recognised. My work is distinct
from this, focusing an historic symbol of justice that has become largely
redundant and, yet, is still in use today when so few of its viewers know what it
represents.The Newcastle University Humanities Research
Institute (NUHRI), the Catherine Cookson Foundation and the Newcastle
Student Innovation Fun
Staging Repentance: A Critical Discourse Analysis of the Framing of Mediated Confessions During the Chinese Cultural Revolution and Xi’s First Five-year Term
Get PDFPh. D. ThesisSince Xi Jinping became the General Secretary of the Chinese Communist Party (CCP) in 2012, he has tightened ideological control on many fronts. Many refer to Xi as the ‘new Mao’ and some even claim that he creates a sense of the Cultural Revolution (1966-1976). This thesis investigates to what extent the official Chinese media has contributed to the formation of such sentiment through the under-studied area of mediated confessions. I argue that the latest resurgence of mediated confessions is an indication of regression towards Maoist social control. Through Fairclough’s three-dimensional critical discourse analysis, I analyse confession-related news from the People’s Daily from 1966 to 1976, alongside three news clips (2014-2016) and an Anti-corruption Campaign documentary (2016) broadcast on China Central Television. The analysis reveals that through selectively and repeatedly appealing to traditional cultural values and adapting to the political agenda of particular Party leaders, the official media framing of both periods makes the individual, not the CCP, responsible for the pressing social issues. Xi not only revives the practice of Mao’s self-criticism model, but also revamps the framing, which invokes the memory of the Cultural Revolution. The framing is often obsessed with individual leadership, linking Xi directly to Mao through certain historiography, and reinforces the distinction between the powerful CCP and the ordinary people while paying lip-service to their potential inter-dependence. The thesis contributes to understanding the revamped Party disciplinary technique. For those who know the cruel nature of mediated confessions, the practice sends a clear message of intimidation to those who are ready to publicly disagree with the CCP. For those who are oblivious to it, mediated confessions blend into ordinary crime news, which has been part of the CCP’s effort to build a socialist society of ‘rule of law with Chinese characteristics’
Janet : the shape of the hidden : collaborative video practice as research in a poetry archive
Get PDFPhD ThesisThis doctoral project was guided by curiosity aroused by a cursory note written in the margins
of a poetry transcript. It is a speculative investigation into the potential significance of
ephemeral material secreted within the administrative section of the Bloodaxe Archive.
The methodological development of the research has been iterative. It was developed
through the production of collage-like video pieces that incorporate documentary-style video
footage, drawing and photography, as well as spoken word, poetry and sound. The project’s
eventual collaborative and cross-disciplinary approach unfolded through initial observational
work in the archive. The research suggests that the composite, transferable and potentially ‘ever
unfinished’1 nature of video is a useful parallel to the idea of the contemporary archive as
shifting and fragmentary.
The written thesis that accompanies the creative work disseminates my utilisation of
video making as a contemporary tool of archival research. The text also acknowledges
embodied and ephemeral ‘technologies’ associated with collaboration - such as conversation
and gesture - as key parts of the research methodology.
My search for Janet within the ephemeral materiality of the archive was a re-imagining
of the archive as a space for speculation rather than a source of truths. Ideas gathered together
in this thesis address the use of archive ephemera as a starting point for association, invention
and autobiographical reflection
Identification and care of patients at risk of post-stroke dementia
Get PDFPhD ThesisStroke can directly cause cognitive difficulties but also increases the risk of future
dementia. There is often less focus on these consequences during standard care,
which tends to concentrate on physical function. The seven publications described in
this thesis focussed on four aims, which were to: a) describe the impact of cognitive
difficulties post-stroke over time b) understand patient and professional views
regarding current care for stroke-survivors with memory problems c) describe the
acceptability and accuracy of dementia risk prediction models following stroke d)
understand healthcare professional views about how to meet the cognitive needs of
stroke-survivors. A mixed-methods approach was used to address these aims
including: a) A systematic review of studies found there was a tendency towards
cognitive decline, but this was not consistent as patients post-stroke can stabilise or
even recover; b) Semi-structured interviews with i) stroke-survivors reporting memory
difficulties and their family carers and ii) primary and secondary care professionals
consistently reported clear gaps in care for stroke survivors with memory deficits; c)
Harmonisation of international stroke cohorts to externally validate existing dementia
risk prediction models which have not validated well in stroke populations. Further, in
the qualitative interviews, patients, family carers and healthcare professionals
identified challenges to their implementation; d) A national electronic-Delphi survey
found that stroke clinicians believe assessment of post-stroke cognition needs better
integration into services with clarification of when and where this should be done to
streamline access. The gaps in current services mean that the support available to
care for and identify those at greatest risk for dementia is lacking. Patients and carers
should be offered information about the long-term cognitive consequences poststroke.
If required, they should be encouraged to seek assistance in the community
with the aim of being directly referred back into specialist services for assessment
and intervention.NIH
Novel methods for posture-based human action recognition and activity anomaly detection
Get PDFPhD ThesisArti cial Intelligence (AI) for Human Action Recognition (HAR) and Human
Activity Anomaly Detection (HAAD) is an active and exciting research
eld. Video-based HAR aims to classify human actions and video-based
HAAD aims to detect abnormal human activities within data. However, a
human is an extremely complex subject and a non-rigid object in the video,
which provides great challenges for Computer Vision and Signal Processing.
Relevant applications elds are surveillance and public monitoring, assisted
living, robotics, human-to-robot interaction, prosthetics, gaming, video captioning,
and sports analysis.
The focus of this thesis is on the posture-related HAR and HAAD. The
aim is to design computationally-e cient, machine and deep learning-based
HAR and HAAD methods which can run in multiple humans monitoring
scenarios.
This thesis rstly contributes two novel 3D Histogram of Oriented Gradient
(3D-HOG) driven frameworks for silhouette-based HAR. The 3D-HOG
state-of-the-art limitations, e.g. unweighted local body areas based processing
and unstable performance over di erent training rounds, are addressed.
The proposed methods achieve more accurate results than the
baseline, outperforming the state-of-the-art. Experiments are conducted on
publicly available datasets, alongside newly recorded data.
This thesis also contributes a new algorithm for human poses-based
HAR. In particular, the proposed human poses-based HAR is among the
rst, few, simultaneous attempts which have been conducted at the time.
The proposed HAR algorithm, named ActionXPose, is based on Convolutional
Neural Networks and Long Short-Term Memory. It turns out to be
more reliable and computationally advantageous when compared to human
silhouette-based approaches. The ActionXPose's
exibility also allows crossdatasets
processing and more robustness to occlusions scenarios. Extensive
evaluation on publicly available datasets demonstrates the e cacy of ActionXPose
over the state-of-the-art. Moreover, newly recorded data, i.e.
Intelligent Sensing Lab Dataset (ISLD), is also contributed and exploited to
further test ActionXPose in real-world, non-cooperative scenarios.
The last set of contributions in this thesis regards pose-driven, combined
HAR and HAAD algorithms. Motivated by ActionXPose achievements, this
thesis contributes a new algorithm to simultaneously extract deep-learningbased
features from human-poses, RGB Region of Interests (ROIs) and
detected objects positions. The proposed method outperforms the stateof-
the-art in both HAR and HAAD. The HAR performance is extensively
tested on publicly available datasets, including the contributed ISLD dataset.
Moreover, to compensate for the lack of data in the eld, this thesis
also contributes three new datasets for human-posture and objects-positions
related HAAD, i.e. BMbD, M-BMdD and JBMOPbD datasets
Democratising data science : effective use of data by communities for civic participation, advocacy and action
Get PDFPhD ThesisWe live in an age of data, where it is being collected and archived in tremendous volumes
and at great velocity. Smart cities are a good example of how we generate and use data with
the aim of improving the lives of citizens. Cities adopting more technologies and embedding
them in the physical fabric of the city will drastically change the way decisions are made in the
city, in addition to the way citizens interact with the city. Research to date has predominantly
focused on engineering agendas or has narrowly focused on citizens’ participation as passive
producers of data in the smart city. This thesis takes a more holistic approach by focusing
on both the engineering problem-solving agenda and community problem-solving activities.
Taking a participatory research approach, the thesis explores such a context through three case
studies that involve the design, development and analysis of two Community Informatics (CI)
systems. In addition to producing two open-source CI technologies (SenseMyStreet and Data:In
Place) for active citizen participation, this study posits a Citizen Advocacy Framework and
Community-Data Interaction (CDI) model as novel theoretical framings that enable researchers
to discuss and design for the effective use of data by communities. Furthermore, this thesis
provides a practical example of the use of CDI for supporting communities to take local action.
This improved understanding of the relationship between data and communities demonstrates
a better direction for future research and the design of CI technologies as they work towards
democratising data science and enabling the effective use of data by communities for active civic
participation, advocacy and action
Stochastic generators for multivariate global spatio-temporal climate data
Get PDFPhD ThesisIn order to understand and quantify the uncertainties in projections and physics of a
climate model (deterministic model), a collection of climate simulations (an ensemble) is
typically used. Given the high-dimensionality of the input space of a climate model, as well
as the complex, non-linear relationships between the climate variables, a large ensemble is
often required to accurately assess these uncertainties. If only a small number of climate
variables are of interest at a speci ed spatial and temporal scale, the computational and
storage expenses can be substantially reduced by training a statistical model on a small
ensemble. The statistical model then acts as a stochastic generator able to simulate a
large ensemble, given a small training ensemble. Previous work on stochastic generators
has focused on modeling and simulating individual climate variables (e.g. surface temperature,
wind speed) independently. Here, we introduce a stochastic generator (trivariate
stochastic model) that jointly simulates three key climate variables. The parameters of
this nonstationary global model are estimated with a sequence of marginal likelihood functions
using large-scale parallelisation across many processors for more than 80 million data
points. We demonstrate the feasibility of jointly simulating climate variables by training
the stochastic generator on ve ensemble members from a large ensemble project, and
assess the stochastic generator simulations by comparing them to the ensemble members
not used in training.
The multivariate spatio-temporal model introduced in Chapter 4 was published in the
Journal of Agricultural, Biological and Environmental Statistics (Edwards et al., 2019).
The theory of marginally parameterised models and stepwise maximum likelihood estimation
introduced in Chapter 3 was submitted to the Journal of Computational Statistics
and Data Analysis (Edwards et al., 2018)
Design and development of a community based micro-hydro turbine system with hydrogen energy storage to supply electricity for off-grid rural areas in Tanzania
Get PDFPhD ThesisMicro-hydropower plants are used to supply electricity to the rural and off-grid areas of most developing countries like Tanzania. Their power capacity ranges from 5 kW to 100 kW which is equivalent to supply electricity from few households to several villages. The challenges that have influenced to undertake this research project are centred on the possibility of designing and developing a cost effective micro-hydro turbine system that can meet the dynamic load demand from the rural off-grid users and at the same time achieve high energy utilization efficiency with minimum energy losses using integrated renewable energy storage technologies such as hydrogen energy storage. The methods used in this research study are based on the field work and site data measurements together with power and energy determination as inputs to system design, modelling and simulation which will determine system characteristics. The results from data analysis show that the feasible water flow discharge for the micro-hydropower plant is 0.45 m3/s with the gross head of 25m which gives a turbine power of 79.5 kW and generator power of 75.5 kW as a power supply. On the other hand, results of the demand power analysis from the case study village shows the load profile has a low demand power of 8.42 kW and high demand power with peak power of 101.8 kW during the evening hours with the daily average energy of 1,114.38 kWh/d while the micro-hydropower can produce a maximum energy supply of 1,812 kWh/day.When supplying power to the load demand, the results show that the micro-hydro system produces excess power of up to 60 kW during low demand hours. In additional to this excess power production, it is also noted that the power supply is not sufficient to supply power during the peak hours of the day. So, in order to supply this peak power deficit, an energy storage system is introduced to store the produced excess electrical energy from the micro-hydro turbine system during the off-peak hours and then export it during the peak hours. Several energy storage options have been studied and analysed and based on the optimization results, the following system has been selected, i.e. micro-hydro turbine system with an electrolyser system and hydrogen fuelled internal combustion engine-generator system. The use of excess electricity to supply to the electrolyser system reduces the excess power to the dump loads to a minimum which results to an increase in the plant capacity factor and make the micro-hydro turbine system more energy efficient
Elucidation of alkane metabolism in the filamentous fungi Ascocoryne sarcoides
Get PDFPh. D. ThesisAscocoryne sarcoides has been reported to produce a variety of secondary metabolites
such as linear and cyclic alkanes that are suitable for biofuel applications. Alkanes and
alkenes are important as they are fully compatible with current fuel infrastructure. The
genetic and biochemical basis for the biosynthesis of linear alkanes in fungi is not known
and routes for cyclic alkane biosynthesis in any domain remains to be established. In this
thesis, A. sarcoides was able to grow robustly in chemically-defined media in which linear,
but not cyclic, alkanes are the sole carbon source, providing evidence for fungal
degradation of alkanes. To establish alkane metabolic pathways in A. sarcoides, the genome
and metabolome of six publicly available A. sarcoides isolates were examined. The
genomes of all six isolates were sequenced, assembled and annotated. For each isolate,
over 10, 000 gene products were identified by combining expression data with Hidden
Markov machine learning. Each genome and predicted proteome achieved over 90%
complete annotation against BUSCO’s database, considered the threshold for a high-quality
dataset. No homology to known alkane producing genes were detected in any A. sarcoides
isolates. By integrating annotations, pathway mapping and gene ontology with comparative
analysis, hypothetical pathways for alkane degradation (via ALK-like P450), linear alkane
biosynthesis (via fdc1-mediated fatty acid decarboxylation/decarbonylation) and cyclic
alkane biosynthesis (via lipid lyase route) are proposed. These findings provide candidate
genes for downstream heterologous expression and have the potential to increase the
available toolkit for advanced biofuel applications. Solvent extraction and stir bar sorptive
methods coupled to GC/MS were used to screen for biogenic hydrocarbon metabolites. The
solvent extraction method did not identified the presence of biogenic alkanes. Moreover,
results from SBSE were inconclusive in establishing A. sarcoides as an alkane producer due
to exogenic alkane contamination and will require further method development.BBSR
Factors affecting the evolution of mimicry
Get PDFPh. D. Thesis.Mimicry, where an undefended species resembles a defended species (Batesian mimicry) or where two or more defended species resemble one another (Müllerian mimicry) is one of the most fascinating examples of natural selection in nature. However, even after more than 150 years of research, there are still outstanding questions. One of the biggest of these is: Why do some mimics resemble their models more closely than others? Several hypotheses have been proposed to explain this, yet few have been tested experimentally. To do this, I collected images of museum specimens of real-life model-mimic pairs using a hyperspectral scanner. I then analysed these images to measure the similarity of model-mimic pairs to a potential avian predator. I then investigated how these measures were affected by three factors which have previously been suggested to influence mimetic similarity: the palatability of the mimic, the climate of the area where the mimic is found and the size of the mimic. None of these factors had a significant effect on any measures of similarity. I then performed two behavioural experiments using domestic chicks (Gallus gallus domesticus) as predators of artificial prey, in order to determine whether the nutritional value of prey influences the degree to which predators discriminate between models and Batesian mimics. I found no direct evidence to support this hypothesis. When taken together, the results of my experiments highlight how much there is still to learn about mimicry as well as the need to test many of the hypotheses surrounding it