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Critical success factors for ICT integration in agri-food sector: pathways for decarbonization and sustainability
Get PDFA decarbonized agri-food sector may provide consumers with nutritious, secure, and reasonably priced food with a lower carbon impact. Decarbonizing the agri-food sector is intricate and necessitates a holistic strategy. Technological advancements, like Information and Communication Technologies (ICT), might be the solution. This study analyses the critical success factors (CSFs) for ICT integration in the agri-food sector in the Western and North Western States of India based on empirical data collected and analyzed. The study proposes a framework that determines and ranks the significant factors for ICT integration in the agri-food sector to achieve the decarbonization goals by utilizing the fuzzy evidential reasoning approach (FERA) and the evidential reasoning approach (EFA). The factors are examined based on the Technological, Organization, and Environmental (TOE) criteria. The results show that the most significant factors contributing to the effective implementation of ICT in the agri-food sector are continuous innovation and R&D, supportive policies and regulations, and cost-effectiveness. The results will assist managers and decision-makers in creating effective policies and making knowledgeable choices that will support sustainable growth in the agri-food industry by lowering carbon emissions through effective ICT integration.Cleaner Engineering and Technolog
Accelerated fatigue measurements using a vibration assembly which loads two specimens simultaneously
Get PDFFatigue testing is notoriously slow, requiring many hours to obtain 1 data point on an S–N curve. Such tests can be accelerated somewhat using vibration-based techniques, which load on the order of kHz, such that tests which would traditionally have taken multiple days can now be completed in a few hours. This work presents a novel assembly that can further accelerate fatigue testing campaigns by loading multiple specimens simultaneously. The assembly has two resonant modes of interest. The lower frequency mode puts a slightly higher strain on one specimen, and the higher frequency mode puts a slightly higher strain on the other specimen. By alternating between the two modes, fatigue loads are rapidly applied until one specimen fails, during which the unfailed specimen will have experienced the same number of cycles, and therefore has less time remaining until its own failure. The test is paused while the failed specimen is removed and replaced by a fresh specimen. Testing continues in this fashion, alternating between the two resonant modes, until a sufficient number of data points have been maintained to map out an S–N curve. Because the assembly loads two specimens simultaneously, the system is up to twice as efficient compared to testing all specimens individually.This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-21-1-0437.Journal of Applied Mechanic
Developing a framework to facilitate a radical innovation culture in mature manufacturing organisations
Get PDFThe main purpose of this project was to investigate the impact of organisational
culture and leadership practices on radical innovation enhancement in mature
manufacturing organisations. The project is intended to advance understating of
the key role played by these factors in this vital sector. This study was exploratory
and interpretative in nature, and a social constructivist approach using inductive
design was found appropriate for achieving its major goals. A qualitative
approach was adopted. The study seeks to provide data that will help address
the research gap. This data was gathered using a combination of semi-structured
interviews, focus groups, and observation. A theoretical framework was designed
to address the research questions and objectives.
The research data was collected through the use of in-depth focus interviews.
Participants were recruited from 18 large mature manufacturing organisations.
After 21 interviews, a saturation point was reached. The findings of the study
presented a variety of factors in the domains of culture, values, and setting that
were identified as significant in affecting leadership performance.
The results of this study proposed a framework with recommended interventions
to facilitate a culture of radical innovation in mature manufacturing organisations.
The emerged themes of the study played a key role in developing this framework.
The findings should make a major contribution to the field of leadership and
radical innovation and the process of change management, by providing
empirical data on the significance of leadership's crucial role in creating a culture
of radical innovation in the mature manufacturing sector in general.PhD in Manufacturin
Robust autonomous navigation for UAVS in urban environments using machine learning
No full textGuo, Weisi - Associate SupervisorUrban canyons, characterized by their high-rise buildings and dense infrastructure, pose
significant challenges to Unmanned Ariel Vehicle (UAV) navigation primarily due to the
obstruction and interference of Global Navigation Satellite Systems (GNSS) signals.
These challenges include multipath effects, where signals bounce off multiple surfaces
before reaching the receiver, leading to inaccurate positioning data, and direct signal
blockage, which causes intermittent loss of satellite visibility. The reliance on GNSS for
UAV navigation in such environments is further compromised by potential denial of
service, whether intentional or unintentional, through interference. These limitations
highlight the need to explore alternative or supplementary navigation technologies to
ensure the safe and efficient operation of UAVs in urban settings.
To address these challenges, this research begins by focusing on reducing sensor errors
in both GNSS and Inertial Navigation Systems (INS). Specifically, data-driven
approaches have been proposed to mitigate errors from individual sensors. For INS, a
Gated Recurrent Units (GRU)-based error prediction algorithm, trained on labelled sensor
data, is utilised to reduce the effects of random walk caused by the sensor’s stochastic
noise, which accumulates over time. For GNSS, a GRU classification algorithm is
employed to detect and exclude Non-Line-Of-Sight (NLOS) signals from the list of
available GNSS satellites. Once NLOS signals are removed, the remaining signals are
ranked based on their contribution to Geometric Dilution of Precision (GDOP). The top
10 ranked signals are then used to compute the receiver's position, resulting in improved
positioning accuracy and greater reliability over time.
Building on these initial advancements, this thesis proposes a robust federated multi-
sensor fusion architecture to further enhance positioning accuracy. The proposed
architecture integrates GNSS and INS positioning, with additional enhancements
provided by GRU-based corrections. Furthermore, Monocular Visual Odometry (VO)
and a barometer are incorporated to refine positioning in GNSS-degraded environments.
A novel Bayesian Long Short-Term Memory (LSTM) model with Monte Carlo dropouts
is introduced to generate stochastic outputs, enabling the system to estimate navigation
integrity by predicting position distributions and calculating Protection Levels (PL). By
quantifying the reliability of navigation data, this approach ensures the safety and
accuracy required for autonomous UAV operations in complex urban environments.
The proposed architecture was validated using a comprehensive simulation setup. This
includes GNSS Hardware-in-the-Loop (HIL) simulations with the Spirent GSS7000 and
OKTAL-SE Sim3D for realistic modelling of multipath effects, as well as high-fidelity
VO sensor data generated through AirSim and Unreal Engine. These simulations replicate
urban canyon scenarios, accounting for variations in GNSS signal quality and adverse
weather conditions, providing a robust environment to test navigation performance.
The results indicate significant advancements in UAV navigation, including a 22.1%
reduction in the 95th percentile horizontal error compared to state-of-the-art federated
fusion approaches and a 98% reduction in misleading integrity information relative to
traditional Extended Kalman Filters (EKF). These improvements demonstrate the
architecture’s ability to deliver accurate, reliable navigation with a quantifiable measure
of integrity, even in the most challenging urban environments.
This research has profound implications for the future of Urban Air Mobility (UAM).
UAM applications, such as passenger air taxis, last-mile deliveries, and emergency
response, rely on precise and reliable navigation systems to ensure safe operation in urban
settings. The ability to provide integrity information—quantifying the reliability of
positioning data—is crucial for regulatory compliance, operational safety, and public trust
in autonomous aerial systems. By addressing the challenges of GNSS-degraded
environments and offering a robust framework for navigation with measurable integrity,
this study lays a strong foundation for UAM. It enables scalable, reliable, and safe
integration of UAVs into urban airspaces, bringing the vision of autonomous urban
transportation closer to reality.Engineering and Physical Sciences Research Council (EPSRC)PhD in Aerospac
Improving pyrolysis oil processing via electrochemistry for plastics and biomass feedstock recycling
No full textJiang, Ying - Associate SupervisorThe environmental pressures of plastic waste accumulation and fossil fuel
dependency have driven interest in technologies that enable the sustainable
conversion of waste into valuable products. Pyrolysis offers a promising route for
recycling biomass and plastic feedstocks into liquid fuels and chemical
intermediates. However, the resulting pyrolysis oils are typically unstable,
oxygen-rich, and incompatible with existing fuel infrastructure. This thesis
investigates electrochemical hydrogenation (ECH) as a low-temperature,
electrically-driven method for upgrading these oils under mild conditions using
water as a hydrogen source, supporting circular economy goals.
The research combines model compound studies with real pyrolysis oil
experiments, using a PtRu/ACC (platinum–ruthenium on activated carbon cloth)
catalyst. Benzoic acid exhibited complete selectivity for cyclohexane carboxylic
acid (100%) under optimised conditions. Notably, the presence of phenol
enhanced benzoic acid conversion by up to 10%, due to a novel hydrogen-bond-
assisted mechanism proposed in this work. This mechanism, which facilitates
adsorption and lowers activation barriers, was supported by density functional
theory (DFT) calculations and represents a previously unreported pathway in
electrochemical hydrogenation. When applied to real bio-oils derived from
pinewood and wheat straw, ECH reduced oxygenated species and increased
alcohol content in the aqueous phase potentially improving both stability and
energy content.
The oily phase was treated in methanol with conductivity enhancers. While NaCl
improved reactivity, it caused significant catalyst degradation.
Tetrabutylammonium hexafluorophosphate (TBAHFP), initially considered less
corrosive, led to greater catalyst degradation but higher conversion of identifiable
compounds, highlighting a trade-off between catalyst durability and product yield.
Advanced characterisation (GC-MS, FTIR, SEM, EDS, XRD, Raman) confirmed
these transformations and catalyst changes.iii
This work offers mechanistic insight and practical guidance for advancing
electrochemical upgrading of pyrolysis oils, providing a foundation for scalable,
low-carbon processes that integrate waste into the energy and chemical sectors.PhD in Energy and Powe
An Artificial Intelligence approach towards screening of the food matrix and digestion effects in predicting phytochemical bioaccessibility
No full textKubo, Mirian - Associate SupervisorFunctional foods are recognised to confer additional health benefits other than
basic nutrition. These desirable characteristics can be attributed to specific
bioactive compounds, such as vitamins or polyphenols. While the topic of health
claims motivates extensive research, there is a notable gap in addressing how
the carrier functional food will impact the gastrointestinal journey of a substance
of interest. This consideration is crucial, as suboptimal food designs can lead to
a compound being degraded or excreted instead of absorbed in a usable form, in
which case the post-consumption effects are negligible.
The underlying physicochemical mechanisms occurring during digestion are
complex. Previous studies provide conceptual knowledge regarding what factors
impact a compound’s absorption, but there is a need for methodologies that
translate this understanding into practical decision-support systems.
In this thesis, a systematic approach for the study of a compound’s absorption as
a function of its carrier food’s properties is presented, based on systematic and
empirical screening experiments. The subjects of study were fat-soluble
compounds, for which it is established that the main limiting step in absorption is
the release from the food and solubilisation in the digesta (bioaccessibility). In the
case of vitamin E, high and invariable bioaccessibilities were observed across all
formulations (~85%), but curcuminoids presented a wide variability of low and
moderate bioaccessibilities (~12%-~55%) which were investigated further. The
results indicated that variations in macronutrient concentration, as well as other
factors, presented strong associations to this variability (p-value < 0.01).
These insights were used to develop a proof-of-concept mathematical model
capable of generalization. Its preliminary estimations closely resembled the
observations (<10% relative error), setting high expectations for future studies.PhD in Environment and Agrifoo
Robust optimisation of wing aerostructural response
Get PDFThe life cycle of an aeronautical product is characterised by an increasing design
certainty and a decreasing in design freedom over time. The uncertainty in the design
and in the manufacturing process may be quantified to assess its impact on the aircraft
performance and on the likelihood of meeting the performance requirements, given
the design margins and constraints. One of the major challenges in this process is
the so-called curse of dimensionality, where the associated computational cost grows
exponentially as a function of the number of random variables and intractable integrals.
This challenge is addressed by exploring how recent advances in numerical methods
and in machine learning might be used in uncertainty quantification and in robust
optimisation.
This work investigates how to solve the following industrially relevant aeronautical
problem: How to perform the robust optimisation of wing twist (jig shape) to balance
aerodynamic performance and risk of exceeding the design loads margins in the presence
of structural uncertainties? To address this problem, this thesis presents theoretical
and algorithmic advances in probabilistic regression, uncertainty quantification and
Bayesian optimisation, demonstrating and expanding its engineering applications. The
primary aim is to formulate and solve the robust optimisation challenge using Gaussian
processes and Bayesian optimisation of a wing subject to structural uncertainties and
design load constraints. The problem is formulated as the evaluation of the impact
of the wing jig shape twist angles, bending and torsional stiffness uncertainties on
the overall lift-to-drag ratio, shear, moment and torque loads across the wingspan
for different load cases and considering design load constraints. As secondary aim,
a variational approach for inference is investigated since the required mathematical
machinery is rarely tractable. It is concluded that probabilistic methods greatly expand
the ability to learn and infer from data yielded by highly complex simulations.PhD in Aerospac
Creating more viable safety recommendations in accident investigation by revising the human factors intervention matrix (HFIX)
Get PDFInternational Journal of Industrial Ergonomic
Toxicity Assessment of Brominated Haloacetic Acids
Get PDFReport from Tara Consulting is provided.A toxicological evaluation was undertaken to identify the most sensitive point of departure (PoD) for each brominated haloacetic acid, which was then used to determine a health-based guidance value (HBGV), from which a drinking water guideline value (GV) was derived
Command governor for impact-angle guidance to fast targets under field-of-view constraint
Get PDFThis paper introduces a command governor for two-phase impact-angle control guidance schemes applicable to air-to-air engagements. Although two-phase guidance is effective in surface-to-surface interception, it shows limitations for aerial targets with speeds similar to or greater than those of the interceptor. To improve the switching guidance scheme for anti-air missiles while complying with the seeker field-of-view constraint and terminal impact-angle requirements, we introduce a command governor that generates a look-angle command to track during the first guidance phase. The proposed command governor integrates a correction policy based on the solution characteristics in the first phase and prediction-based correction in the second phase. Hence, interception of fast aerial targets with a specified impact angle is achieved by incorporating the proposed command governor into the two-phase guidance scheme. An analysis of the region of feasible initial conditions ensures the capturability of the guidance law, and numerical simulations illustrate its effectiveness for interception at specified impact angles.This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00251551).Aerospace Science and Technolog