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Low Energy, Passive Acoustic Sensing for Wireless Underwater Monitoring Networks
Full text linkPh. D. ThesisThis thesis presents the research conducted to develop low energy passive acoustic monitoring
(PAM) algorithms. There are many signal processing techniques and machine learning
systems which are capable of detecting and classifying target signals. However, this project
aims to produce PAM detection and classification results using a low energy budget. The
benefit of using this approach is that physical devices can be developed and deployed in
open sea for several months using only battery power. This opens up the deployment area
to very deep water where power sources are not readily available. Using passive acoustic
communication to relay the detection data produced by the algorithm, it is expected that
these systems could form an underwater network of sensor nodes.
There are three targets for passive acoustic detection/classification included in this thesis,
which are motorised surface vessels, cetacean clicks and cetacean whistles. The surface
vessel detection method is based on a low energy implementation of Detection of Envelope
Modulation On Noise (DEMON). Vessels produce high frequency modulated noise during
propeller cavitation which the DEMON method aims to extract for the purposes of automated
detection. The vessel detector design has different approaches with mixtures of analogue
and digital processing, continuous and duty-cycled sampling/processing. The detector
has been integrated with a low cost/power acoustic modem platform to provide acoustic
communication of data in near real time. The vessel detector has been deployed at 20m depth
for a total of 84 days in the North Sea providing a large data set, which the results are based
on.
Open sea field trial results have shown the detection of single and multiple vessels with
a 94% corroboration rate with local Automatic Identification System (AIS) data. Results
have shown additional information about the detected vessel, such as the number of propeller
blades, can been extracted solely based on the detection data. The attention to energy efficiency
has led to an average power consumption of 11.4mW enabling long term deployments
of up to 6 months using only four alkaline C cells. Additional battery packs and a modified
enclosure could enable a longer deployment duration. As the detector was still deployed
during the first UK lockdown, the impact of Covid-19 on North Sea fishing activity has been
captured in the results.
Cetacean click detection is based on identifying and classifying the high frequency
impulsive click trains created by cetaceans during navigation and foraging. A low energy
method of detecting these vocalisations is proposed alongside a statistical based method of
classification. The algorithm developed was tested using real recordings of cetacean activity
and comparisons have been conducted against a commercially available cetacean monitoring
system. The results show that the energy efficient algorithm produces comparable results to
the commercial system when real recordings are processed.
The cetacean whistle detection algorithm is based on a low energy phase locked loop
(PLL) technique. PLL methodology has been adapted for this project to aid in developing
a low energy approach to detecting cetacean whistles by tracking the sweeps in frequency
they produce. Results are based on offline processing using real recordings of these animals.
The results have shown a 75% success rate when comparing against human analysis of the
recording.
Future work includes the further development of the cetacean related algorithms into fully
deployable, battery-powered, nodes for open sea field trails. The future work related to vessel
detection includes adding a tracking feature to the passive acoustic monitoring technology.Engineering and Physical Sciences
Research Council (EPSRC
Induction of plasticity in subcortical structures and its application in spinal cord injury
Full text linkPh. D. ThesisMost current non-invasive plasticity protocols target the motor cortex and its corticospinal projections. Approaches for inducing plasticity in sub-cortical circuits and alternative descending pathways such as the reticulospinal tract (RST) are less well developed.
The overall aim of this thesis was to gain a better understanding of the extent to which corticospinal transmissions are altered after spinal cord injury (SCI) and to explore the mechanisms of non-invasive stimulation protocols at the cortical and subcortical level.
In the first study, transcranial magnetic stimulation was used to elicit motor-evoked potentials (MEPs) in the biceps brachii using different coil orientations, which allows for preferential activation of different neural elements. Analysis of MEP latencies suggests that differences between MEPs elicited by specific coil orientations may not be fully preserved in humans with cervical SCI, both in the biceps and in more distal muscle groups.
In a second study, we developed a novel associative stimulation paradigm, which paired loud acoustic stimuli with transcranial magnetic stimulation over the motor cortex in healthy participants and observed enhanced motor output after stimulus pairing ended. Electrophysiological measurements in humans and direct measurements in monkeys undergoing a similar protocol implicate corticoreticular connections as the most likely substrate for the plastic changes.
Finally, we used a custom built device to deliver precisely paired auditory clicks with electric stimulation to the muscle. We observed changes in electrophysiological measurements consistent with the induction of sub-cortical plasticity in the biceps muscle. We then used the same protocol to target the triceps muscle in individuals with SCI over the course of 4 weeks. Notably, we did not observe the same changes as in the biceps muscle, suggesting that elbow flexors and extensors have a different potential for plasticity, perhaps due to a differential control of flexor and extensor motoneurons by corticospinal and reticulospinal pathways.International Spinal Research Trus
Using audit and feedback to improve colonic polyp detection, qualitative studies within the national endoscopy database automated performance reports to improve quality outcomes trial (NED APRIQOT)
Full text linkM.D ThesisColorectal cancer (CRC) arises from polyps, and polyp detection and resection at
colonoscopy is pivotal in preventing CRC. Colonoscopists with a low polyp detection rate
have a higher rate of CRC after colonoscopy. The National Endoscopy Database Automated
Performance Reports to Improve Quality Outcomes Trial (NED-APRIQOT) is a randomised
cluster control trial of electronic audit and feedback (A&F) in English endoscopy centres. This
MD aimed to (1) assess the acceptability of colonoscopy key performance indicators (KPIs);
(2) develop an evidence-based and theoretically informed behaviour change intervention
(BCI), an A&F endoscopist performance report, for implementation in the trial; and (3)
explore pre-trial experiences of endoscopy A&F.
A narrative review of A&F and KPIs in the colonoscopy literature was undertaken. This
informed selection of KPIs for a Delphi consensus, to determine the clinical acceptability of
KPIs available through the NED. A panel of UK experts in colonoscopy, reflecting the varied
professional backgrounds performing endoscopy, undertook three rounds rating statements
and provided free-text comments. A case-mix adjusted mean number of polyps (MNP) was
chosen for the trial.
An A&F behavioural theory review informed the design of a draft BCI. Interviews were
undertaken with 19 endoscopists from six English NHS endoscopy centres, purposively
sampled for clinical background and professional experience. The BCI was iteratively refined
through rounds of cognitive interviews in which participants interacted with and ‘talked
aloud’ about the BCI. The finalised BCI was implemented in the NED-APRIQOT.
These participants also undertook semi-structured interviews exploring current colonoscopy
A&F practices. A framework thematic analysis mapped themes to Feedback Intervention
Theory (FIT) and the Theory of Planned Behaviour. A FIT-based model described A&F’s
intended and paradoxical effects on endoscopist behaviour. Detection and patient safety
were dependent on coaching, team behaviours and unit-leads managing underperformance.
Future endoscopy A&F interventions should consider targeting behaviours using theoretical
models
The potential for carbon capture and utilization (CCU) for the state of Kuwait
Full text linkPhD ThesisCarbon Capture and Utilization (CCU) is a crucial enabling technology that supports
delivery of the dual challenges of maintaining fossil fuels as a key energy source, whilst
simultaneously dramatically reducing the associated CO2 emissions.
This thesis aims to develop a realistic database of CO2 emission sources in the state of
Kuwait. The research then investigates the potential of deploying CCU in Kuwait, currently one
of the highest carbon emitting countries in the world.
After identifying the major sectors responsible for CO2 emissions, both 'top-down' and
'bottom-up' approaches were used to aggregate data from these sectors. The Emission Factors
(EFs) were acquired from open literature such as the Intergovernmental Panel on Climate Change
(IPCC). The analysis then explored the stakeholders’ inclinations towards CCU. Both qualitative
and quantitative surveys methods were conducted in the form of focus group discussions and the
Information- Choice Questionnaire (ICQ), respectively.
The Kuwaiti power sector proved to be the predominant stationary source of carbon dioxide
(CO2) emissions (42%) due to high regional demand for electricity and water. The chemical
industry ranked second in this analysis with a significant share of CO2 emissions (26%) which
was attributed to heavy and energy intensive industries, and this was followed by road
transportation (16%).
The total process emissions were covered in this analysis for the first time which explains
the variation between the real carbon footprint of Kuwait 98 Mt CO2/y and both the World Bank
91.03 Mt CO2/ y (WBR, 2006) and International Energy Agency 69.82 Mt CO2/ y (IEA, 2010b)
with differences of 7.7% and 40%, respectively.
The geographical distribution of CO2 emissions was analysed, showing that high emission
facilities are clustered mainly in the southeast which is the predominant industrial area in the
state. This distribution could potentially be favourable for the formation of a ‘capture cluster’
which could reduce the overall cost of carbon capture deployment as a route for a sustainable
carbon mitigation practice. If the Kuwait government diversify its economy towards non-oil
bases, the carbon footprint of the state will increase from 118 to 126 Mt/y.
Overall, there was a positive attitude among all stakeholders, across a number of different
sectors, regarding the potential of deploying CCU technology. However, some technical and
economic barriers should first be addressed in each of the sector facilities since they are not
designed to be retrofitted with carbon capture units.
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In general, limited flexibility in Kuwaiti facilities with regard to being retrofitted with CCU
technologies, and the impact of this process on their efficiencies, represent the main technical
obstacles in the State. In addition to the technical barriers of reusing the existing high-pressure
natural gas infrastructure for CO2 transportation and managing the injecting process of CO2 into a
deep saline aquifer. From an economic aspect, the economic burden of introducing this
technology to various institutions in the country will vary significantly depending on the lifetime
and operating conditions of the current facilities. Oxy-fuel combustion appears to be the most
economically attractive technology with its cumulative cost equivalent to approximately one third
of the cost of post-combustion.
The key actions required to fully understand the potential of CCU in the state of Kuwait
include developing new environmental regulations, extending the scope of the analysis to include
techno-economic analyses, deployment of more pilot plants for CO2-EOR in the north of Kuwait,
and carrying out field optimization studies for the saline aquifer reservoirs
A flat extension theorem for truncated matrix-valued multisequences
Full text linkPhD ThesisGiven a truncated multisequence of p × p Hermitian matrices S := (Sγ1,...,γd
) (γ1,...,γd)∈Nd
0
0≤γ1+···+γd≤m
, the
truncated matrix-valued moment problem on R
d asks whether or not there exists a p×p positive
semidefinite matrix-valued measure T, with convergent moments of all orders, such that
Sγ1,...,γd =
Z
· · · Z
Rd
x
γ1
1
· · · x
γd
d
dT(x1, . . . , xd)
for all (γ1, . . . , γd) ∈ N
d
0 which satisfy 0 ≤
Pd
j=1 γj ≤ m. When such a measure exists we say
that T is a representing measure for S. We shall see that if m is even, then S has a minimal
representing measure (that is, Pκ
a=1
rank Qa is as small as possible) if and only if a block matrix
determined entirely by S has a rank-preserving positive extension. In this case, the support
of the representing measure has a connection with zeros (suitably interpreted) of a system of
matrix-valued polynomials which describe the rank-preserving extension. The proof of this
result relies on operator theory and certain results for ideals of multivariate matrix-valued
polynomials. Our result subsumes the celebrated flat extension theorem of Curto and Fialkow.
We shall pay particularly close attention to the bivariate quadratic matrix-valued moment
problem (that is, where d = 2 and m = 2)
Software-in-the-Loop combined Artificial Intelligence for Optimised Design and Dynamic Performance Prediction of Floating Offshore Wind Turbines
Full text linkPhD ThesisFloating Offshore Wind Turbines (FOWTs) have shown a promising future due to the
goal of Net Zero emissions by 2050. However, the highly coupled nonlinear performances
of FOWTs bring many challenges to the implementation of numerical and basin
experimental methods in design and optimisation. This PhD project proposes an
innovative method, named SADA (Software-in-the-Loop combined Artificial
Intelligence Method for Dynamic Analysis of Floating Wind Turbines), to optimise the
design and predict dynamic performances of FOWTs. SADA is built based on a coupled
aero-hydro-servo-elastic programme DARwind and Machine Learning Algorithms.
Firstly, the concept of Key Disciplinary Parameters (KDPs) is inspired by FOWT-related
disciplinary theories. Secondly, DARwind will take continuous action through the
Software-in-the-Loop (SIL) model to obtain more accurate prediction results. Thirdly,
SADA can build data sets and analyse deep-seated physical laws of FOWTs.
Then, case studies were conducted to prove the feasibility of the SADA method on the
basin experiment data. The results show that the mean values of some physical quantities
can be predicted by SADA with higher accuracy than the original DARwind simulation
results. In addition, full-scale case studies were conducted by extending SADA to
engineering applications, though some design parameters are not accessible. Furthermore,
other physical quantities that cannot be obtained directly in full-scale measurement easily
but are of great concern to industry can also be obtained from a more credible perspective.
The proposed SADA method could benefit the wind industry by taking advantage of the
numerical analysis method and AI technology. This brings a new and promising solution
for overcoming the handicap impeding direct use of traditional basin experimental
technology or full-scale measurement. Therefore, designers in the wind industry can
optimise FOWTs designs to a higher level, thereby achieving a better method of and
maintaining safe operation of FOWTs in a complex sea state
Novel features in accelerometer-based gait analysis for long-term monitoring of Parkinson’s disease : a signature of gait.
Full text linkPhD ThesisParkinson’s Disease (PD) is a neurodegenerative disease that can lead to restricted
or slowed movement, gait impairments and increased risk of falling. Over recent
decades, instrumented gait analysis (IGA) has contributed much to the understanding of gait impairments in PD. Due to the complexity of gait and high clinical
interest a plethora of features have been suggested for gait analysis in the literature pertaining to several groups such as: traditional spatio-temporal (e.g. gait
speed), frequency domain, etc. A subset of these traditional gait features has been
proposed and validated in PD and older adults as a comprehensive model of gait
comprising five factors: pace, rhythm, asymmetry, variability, and postural control.
Analysis of gait may be grouped into the assessment of two types of variability,
namely, within-subject variability which is needed for personal disease management
and inter-subject variability which is useful in quantifying the overall impact of PD
on gait. Advances in wearable technology have led to much smaller devices (e.g.
accelerometers) being commercially available in conjunction with greatly increased
battery lives to the degree that not only lab-based but also continuous recordings
over 7 days (real-world) are possible. Wearable technology-based gait analysis is
indeed emerging as a powerful tool to detect early disease and monitor progression.
Data recorded as part of the ICICLE-GAIT 1
study provides acceleration data for
over 100 people with PD and age-matched control subjects in both lab and realworld conditions. These datasets form the basis for the development of a new Phase plot methodology for gait analysis in PD. In this thesis I present a novel methodology for both assessing PD and tracking individual disease progression over multiple
timescales. To accomplish this, I introduce a new feature domain, the Phase domain,
based on a particular type of recurrence plot known as a Poincar´e plot. Poincar´e
plots are sometimes referred to in the literature as return maps, self-similarity plots
or Phase plots. Phase plots were being used in the early 1990s in ECG studies to
produce self-similarity plots of beat-to-beat intervals. This technique proved to be
reliable in detecting atrial fibrillation. The rare instances of its application to other
fields are very limited and do not demonstrate any modification or development
beyond that which has been used in ECG studies for decades. I develop methodology for application to gait analysis and, indeed, any cyclical biosignals. In this
thesis I used the data from the ICICLE-GAIT study to demonstrate that with specific modifications and newly identified features (comprising the Phase domain), this
novel Phase plot methodology is highly applicable to gait analysis within PD and
provides a framework for: (i) identifying and characterising PD and (ii) individual
disease tracking over the years following diagnosis. Throughout these analyses, traditional gait features serve as an established reference and benchmark. I employ
statistical methods, such as non-linear mixed effects models and Statistical Parametric Mapping, to model PD progression and assess the clinical utility of Phase
plots. I also used Discrete-Time Markov chain modelling, longitudinal analyses, and
functional principal components analysis to demonstrate that Phase plots provide
an objective, personalised, and clinically relevant signature of gait. In the case of
PD patients (and controls to a lesser extent) four distinct Phase plot Types emerge
and occur with high within-subject reproducibility, hence the signature interpretation. Many features within the Phase domain proved to be highly sensitive to the
disease (people with PD versus controls). Using lab-based data, the Phase domain features outperformed traditional spatio-temporal features in classifying PD. Each
domain of features performed similarly well in the prediction of MDS-UPDRS 2
(a
useful proxy for PD progression). Specifically, part III of the UPDRS scale was
used as this relates to motor function. In real-world conditions Phase plot features
showed sensitivity to disease state and physical capability across multiple timescales
e.g., daily fluctuations, and also across 18-month follow up time points. The Phase
plot-based signature of gait is validated under lab-based conditions to reflect participants’ capacity for gait as well as under real-world conditions as a compact means
of monitoring PD and walking performance through gait
Advanced adaptive modelling approaches in the evolution of vector/cell manufacturing processes
Full text linkPhD ThesisThe field of cell gene therapy has seen significant progress in recent years. The last
decade has seen the licensing of the first Cell Gene Therapy (CGT) treatments in
Europe and clinical trials have demonstrated safety and efficacy in the treatment of
numerous severe inherited diseases of the blood, immune and nervous systems.
Specifically, autologous viral vector-based CGT treatments have been the most
successful to date. However, the manufacturing processes for these CGT treatments
are at an early stage of development, and high levels of complexity, process variability
and a lack of advanced process and product understanding in vector/cell
manufacturing are hindering the development of new processes and treatments.
Here, Multivariate Data Analysis (MVDA) and Machine Learning (ML) techniques,
which have not yet been widely exploited for the development of CGT processes, were
leveraged to address some of the main hurdles in the development and optimisation
of CGT processes. Principal component analysis (PCA) was primarily used for feature
extraction to understand the main correlations and sources of variability within the
process data, and to evaluate the similarities and differences between batches.
Additionally, a sparse PCA algorithm was developed to ease the interpretation of the
principal components with a large number of variables present in the dataset.
Predictive modelling techniques were utilized to model the relationships between
process variables and critical quality attributes (CQAs) of the viral vector and cell drug
products. The infectious titres of lentiviral vector (LV) products from both adherent cell
cultures and suspension cell cultures were modelled and predicted successfully and
critical process variables were identified with statistically significant correlations to this
CQA. In cell drug product manufacturing, the LV copy number in the patient’s
transduced cells was also modelled and process parameters in LV manufacturing and
cell drug product manufacturing were linked to this CQA.
Overall, the modelling process recovered valuable information from historical process
data from the early stages of process development. This data frequently remains
unexploited, due to its commonly truncated and unstructured nature; however, this
work showed that MVDA/ML techniques can yield beneficial insights despite less than
ideal data structure and features.GlaxoSmithKline and the Engineering and Physical
Sciences Research Counci
Spatial optimisation for resilient infrastructure services
Full text linkPh. D. Thesis.Infrastructure networks provide crucial services to the functioning of human settlements.
Extreme weather events, especially flooding, can lead to disruption or complete loss of these
crucial infrastructure services, which can have significant impacts on people’s health and
wellbeing, as well as being costly to repair. Urban areas concentrate infrastructure and people,
and are consequently particularly sensitive to disruptions due to natural (and human-made)
disasters. Flooding alone constituted 47% of all weather-related disasters between 1995 and
2015, causing enormous loss of lives and economic damages. Climate change is projected to
further exacerbate the impacts that natural disasters have on cities.
Choices about where to site infrastructure have a significant impact on the impacts of extreme
weather events. For example, investments in flood risk management have typically focussed
on prioritising interventions to protect people, houses and businesses. Protection of
infrastructure services has either been a bonus benefit of flood defence protection of
property, or been implemented by individual infrastructure operators. Spatial planning is a
key process to influence the distribution of people and activities over broad spatial scales.
However, decision-making processes to locate infrastructure services does not typically
consider resilience issues at broad spatial scales which can lead to inefficient use of resources.
Moreover, spatial planning typically requires consideration of multiple, sometimes competing,
objectives with solutions that are not readily tractable.
Balancing multiple trade-offs in spatial planning with multiple variables at high spatial
resolution is computationally demanding. This research has developed a new framework for
multi-objective Pareto-optimal location-allocation problems solving. The RAO (Resource
Allocation Optimisation) framework developed here is a heuristic approach that makes use of
a Genetic Algorithm (GA) to produce Pareto-optimal spatial plans that balance a typical tradeoff in spatial planning: the maximisation of accessibility of a given infrastructure service vs the
minimisation of the costs of providing that service. The method is applied to two case studies:
(i) Storage of temporary flood defences, and (ii) Location of healthcare facilities.
The RAO is first applied to a flood risk management case study in the Humber Estuary, UK, to
optimise the strategic allocation of storing space for emergency resources (like temporary
flood barriers, portable generators, pumps etc.) by maximising the accessibility of warehouses
(i.e. minimising travel times from storing locations to deployment sites) and minimising costs.
The evaluation of costs involves both capital and operational costs such as the length of
temporary defences needed, storage site locations, number of lorries and personnel to enable
their deployment, and maintenance costs. A baseline is tested against a number of scenarios,
including a flood disrupting road network and thereby deployment operations, as well as
variable infrastructure and land use costs, different transportation and deployment strategies
and changing the priority of protecting different critical infrastructures.
Key findings show investment in strategically located warehouses decreases deployment time
across the whole region by several hours, while prioritising the protection of the infrastructure
assets serving larger shares of population can cut costs by 30%. Moreover, the analysis of the
ensemble of all scenarios provides crucial insights for spatial planners. For example, storage
sites in Hull or Hedon, and in the areas of Withernsea and Drax are robust choices under all
scenarios. Meanwhile, the Humber Bridge is shown to play a crucial role in enabling regional
coverage of temporary barriers.
The second case study shows how emergency response strategies can be enhanced by optimal
allocation of healthcare facilities at a regional scale. The RAO framework allocates healthcare
facilities in Northland (New Zealand) balancing the trade-off between maximisation of
accessibility (i.e. minimisation of travel times between households and GP clinics) and
minimisation of costs (i.e. number of clinics and doctors). Results show how c.80% of
Northland’s population lives within a 20 minutes drive from the closest GP, but this can be
increased to 90% with strategic investment and relocation of doctors and clinics. By
accounting for flood and landslide risk, the RAO is used to identify strategies that improve
accessibility to healthcare services by up to 5% even during extreme events (when compared
to the current business as usual service accessibility).
Application to these two problems demonstrates that the RAO framework can identify optimal
strategies to deploy finite resources to maximise the resilience of infrastructure services.
Moreover, it provides an analytical appreciation of the sensitivity between planning tradeoffs
and therefore the overall robustness of a strategy to uncertainty. The method is consequently
of benefit to local authorities, infrastructure operators and agencies responsible for disaster
management. Following successful application to regional scale case studies, it is
recommended that future work scale the analysis to consider resource allocation to protect
infrastructure at a national scaleEngineering and Physical Sciences Research Counci
Socio-ecological indicators for sustainable management of global marine biodiversity conservation using sharks as a model species
Full text linkPh. D. ThesisGlobal biodiversity is disappearing at an unprecedented rate; sharks are currently among the most threatened vertebrate groups with widespread overexploitation leaving 31% of all species at risk of extinction. Since 2009, 17 coastal nations have adopted a precautionary approach banning all commercial shark fishing. However, evaluating effectiveness of these ‘shark sanctuaries’ is impeded by a lack of robust data. Evidence-based conservation urgently requires data against which socio-ecological change can be measured to assess efficacy of policy and management interventions. This thesis takes an interdisciplinary approach to advance understanding of the complexities of shark conservation within one of the world’s principal shark sanctuaries - the Maldives. Historical abundance trends derived from fisher Local Ecological Knowledge (LEK, 87 interviews) showed substantial declines in shark population abundance (>65%) and distribution (>60%) between 1970-2019. Validation of contemporary spatial LEK using Baited Remote Underwater Videos (BRUVs, 50 hours of footage) highlighted the potential of LEK to provide fine-scale distribution data for shark populations in data poor regions. Analysis of BRUVs (464 hours of footage) and citizen science data (2,024 dives) over a 5-year period (2016-2020) revealed historical population declines have now been halted and suggests species abundances are stable following sanctuary implementation. However, positive correlations between prey and reef shark abundance raises uncertainty over the long-term efficacy of sanctuaries, which still permit exploitation of prey species. Interviews with fishers (n = 103) identified correlations between fisher characteristics, perceptions, and support for the Maldives shark sanctuary. Findings identified several management actions that could increase support: increasing stakeholder participation and representation (voice to capture local knowledge); mitigation of the costs associated with fisher-shark interactions and increasing transparency in management decision making. The potential severity and inequity in livelihood costs associated with shark sanctuaries was also highlighted revealing that small-scale reef fishers were disproportionally impacted compared to pelagic tuna fishers. This thesis highlights the importance of integrating human and ecological dimensions into shark conservation to tailor measures more likely to be effective in specific contexts and suggests that low support for sanctuary regulations, fisher-shark conflict and overexploitation of reef resources, could hinder long-term population recovery. Findings outline rapid, cost-effective approaches towards generating priority data to provide a basis for evidence-based management that will help define future efforts to enhance shark conservation in the context of achieving the UN Sustainable Development Goal (SDG) 14.Newcastle University’s Institute for Sustainabilit