The role of business manager attitudes and perceptions in driving climate change risk action in the agricultural sector in Uganda

Much is already known about climate change risk mitigation and adaptation globally. However, much needs to be done to make this knowledge cascaded down to a business manager in the agricultural sector in Uganda. This study aimed to understand the role of business manager perceptions and attitudes in influencing climate change risk action in business organizations in the agricultural sector in Uganda with its particular climatic, social and economic circumstances. An assessment was made of whether and how the climate change risk perceptions of business managers from 16 companies engaged in downstream agricultural processing differ from 15 managers engaged in commercial agricultural production in Uganda. The study utilized a phenomenological approach using comparative case study method. The respondents were selected purposively from managed agriculture processor and producer companies. It is believed that the study of perceptions and beliefs involves uncovering tacit knowledge, knowledge in the minds of managers which cannot easily be articulated and documented. The study therefore made use of George Kelly’s Personal Construct theory and its repertory grid analysis technique for data collection, a very useful tool for making tacit knowledge explicit. The study examined nine risks as elements for the repertory grid exploring how business managers perceive there risks and how such perceptions influence their climate change risk action in the agriculture sector in Uganda. The study also intended to identify if there are variations in climate change risk perception between the agriculture producers and processors in Uganda. The personal constructs generated from respondents during the grid interviews are the units of analysis. The results were analyzed using Content analysis, and Honey’s data analysis procedures. The results indicate that as long as business managers perceive climate change risks to have an effect on their business continuity or survival, their production capacities, their profitability, their marketing decisions, affect their cost of production, influence their investment decisions, there are available response options, and consider that they have the capacity to manage those risks, they will take immediate action to put in place strategies to respond to those climate change risks. There is no appreciable variation in climate change risk perception between producers and processors. The study results provide policy makers an opportunity to understand what concerns business owners along the agriculture value chain for them to respond to climate change risks and also informs business owners the areas of key concern that they have to reflect on as they consider climate change risk strategies

Modelling and investigating nonlinear pulse propagation in nanophotonic periodically-poled waveguides

The recent advancement in fabrication techniques have allowed for periodically-poled nanophotonic waveguides that can exhibit strong second- and third-order nonlinearities, at significantly reduced operating pump levels. In this thesis, we study the evolution of optical pulses in these structures using the unidirectional pulse propagation equation, and demonstrate how the poling period can offer an additional degree of freedom to shape the output spectra of nonlinear waveguides. Also, we introduce a novel architecture for recurrent neural networks that can be trained to predict the spectral and temporal evolutions of a pulse in different nonlinear waveguides. The presented model provides a generalised approach to fast pulse propagation simulation, using a single neural network. Moreover, the networks can also be designed to predict the real and imaginary components of the pulse complex envelope, that allows the retrieval of the pulse phase, and the simultaneous calculation of the spectral and temporal evolutions

Performing the festival : an experiential autoethnography of the festival of Sant’Efisio in Sardinia

This research project considers festivals as sites of transformation, adaptation and negotiation for the communities interacting with their social environment. Employing a case-study strategy, the thesis carries out an in-depth exploration of one of the most celebrated events in the island of Sardinia (Italy): the Festival of Sant’Efisio. This complex celebration has been performed for 368 years to fulfil a vow in honour of the martyr and saint Efisio, who is believed to have saved Sardinia from the plague in the 17th century. The festival includes a multitude of secular and religious events and ceremonies which take place around a four-day pilgrimage. This study is placed within an interpretative phenomenological framework, underpinned by a feminist approach throughout, that considers “performance” as the key theoretical lens to inform the analysis of the following socio-cultural issues in festivals: 1) the display of cultural heritage; 2) community construction and conflict; and 3) gendered practices. Based on ethnographic fieldwork, both in person and online, this project investigates the effects of social and cultural transformations in relation to these issues within the Festival of Sant’Efisio, by addressing how the festival is interpreted, experienced, felt and performed by the people involved. The researcher’s perspective and experience are central to this enquiry and are discussed throughout by means of autoethnography. I suggest that the way people feel in festivals is crucial to understand their socio-cultural significance, as well as their survival through the change of time.Heriot-Watt University scholarshi

Why institutional investor expectations on the speed of the energy transition matter : a complex systems perspective on sustainable investment behaviour

The sustainable finance gap continues to widen despite the urgent need for a sustainable energy transition. Solutions to closing this gap tend to overlook the complexities associated with the energy transition and the role of investor expectations on the speed of the energy transition in investment behaviour. To address these issues, this thesis firstly aims to develop a framework linking investor expectations on the speed of the energy transition and investment behaviour, from a complex systems perspective in a UK context. This can be used for understanding key sustainable finance issues and creating appropriate solutions and policies. This thesis also aims to develop a method for testing the effects of potential policies on expectations and behaviour in a complex environment, under various scenarios. To fulfil these aims, a unique combination of methods is used in the context of sustainable finance involving causal mapping and gamification. The results show that investor expectations can significantly impact investment behaviour in some cases, particularly when energy transition expectations turn negative. This occurs when expectations work simultaneously along multiple pathways and/or feedback loops are initiated, which can trigger non-linear effects in the system. The results also show that significant change in investor expectations and investment behaviour can be induced under scenarios connected to integrating sustainability into valuation, a reversal of support for the energy transition, and through particular scenario combinations. These findings imply that investor expectations on the speed of the energy transition need to be managed carefully when implementing policies and regulations, as there can be adverse effects to sustainable finance flows if expectations turn negative. It also highlights potential scenarios and policies that can trigger changes in sustainable investment behaviour

Functionalisation of polymeric materials with palladium nanostructures for applications in bioorthogonal prodrug activation

The use of bioorthogonal organometallic chemistry to facilitate the localised conversion of a chemotherapy prodrug into an active drug has been explored in a novel approach to targeted drug delivery. Chemotherapeutic prodrugs containing a palladium (Pd)-labile propargyl protecting group have been established, necessitating a suitable method to deliver the Pd trigger to its desired location. To this end, implants that can be inserted into the tumour site and contain catalytic Pd nanoparticles are being developed. This work demonstrates the functionalisation of non-degradable polymeric materials, poly(ethylene glycol) (PEG) microbeads or poly(2-hydroxyethyl methacrylate) (pHEMA)-based hydrogels, with Pd nanostructures. New methods for the in situ preparation of Pd nanoparticles, nanocubes and nanosheets inside these novel polymeric materials are presented. The ability of these materials to facilitate the key depropargylation reaction using a model compound, propargylated-Resorufin, is presented, with Pd-nanosheets displaying superior catalytic activity, entrapment, and reusability. The distinctive nanostructures synthesised within these PEG or pHEMA materials were examined using electron microscopy techniques, also showing the complexity of the polymer networks arising from inclusion of sheared gellan gum as a unique suspending additive. The results presented herein contribute to the development of implantable Pd-containing polymeric materials, capable of the required prodrug activating depropargylation reaction, and preliminary in vitro cytotoxicity tests show promise for the biocompatibility of some of these materials

Acoustic emission propagation through bone tissue with focus on a jaw bone surrogate model

Implants are used to improve quality of life, for example, dental implants can resolve negative effects of tooth loss, however current techniques for monitoring dental implants have limitations. An Acoustic Emission Finite Element framework could reduce limitations, whilst adding more capabilities. To realise this, simulations of AE propagation through an implant-less system are needed. Therefore the aim of this study was to simulate AE propagation through bone tissue. To that end, a material model for bone was developed and implemented into FE, in-conjunction with µCT-image-based 3D rib models created from fifteen rib samples used in the AE experiments. These experiments were then sim ulated in FE – ten of the samples were used to identify viscoelastic parameter β for the material model. The remaining five were used to validate the simulations of AE propa gation through bone. The material model was verified against theory, and the viscoelastic parameter, β, was identified to range from 0.0648 to 0.22 for the ten samples, with no clear correlation with bone sample properties. The material model was validated with three out of the five samples used for validation. Simulation of AE propagation through bone can be accomplished, thus there is potential for development of an AE FE implant monitoring framework

Analysis of multibeam reflector antennas for satellite applications

The demand for high-capacity satellite communication systems has grown exponentially in recent years, driven by the need for high-speed connectivity. As the demand rises, the efficient utilization of satellite resources becomes imperative. This has underscored the importance of developing advanced antenna technologies to meet user requirements. Multibeam Reflector Antennas (MBRA) offer a promising solution by enabling multiple beams, which can simultaneously serve multiple users or regions from a single satellite platform. This technology is one of the preferred architectures for Very High Throughput Satellite (VHTS) systems to achieve the requirements from geostationary orbit (GEO) due to the high gain offered by the system optics. They combine a reflector with an array of feed elements which can generate multiple spot beams. To fully exploit the capabilities of the satellite, multi-disciplinary optimisation becomes a very important topic in the design process of the payload system. In this context, efficient estimation of the multi-beam coverage characteristics becomes a step towards the desired system performance evaluation. This thesis presents the research work carried out towards an efficient way of estimating the beam patterns of multibeam reflector antennas when the number of beams employed is very large. To that end, an in-depth study of reflector antennas for space applications and their analysis is described. HERAS (HEriot-watt Reflector Antenna Solver) tool is presented, an in-house tool developed for the analysis of reflector antennas using Matlab. This tool is used as part of this research as the core element for the rapid estimation of hundreds of beams. Two different configurations of MBRA are considered: Single Feed Per Beam (SFPB) and Array Fed Reflector (AFR) antennas. Different acceleration techniques are developed to efficiently estimate the antenna patterns for each of the configurations. These include a threshold method, interpolation methods or computational techniques. They are essentially based on the reduction of the number of farfield points to be calculated or the size of the numerical integration to be performed for each beam calculation. In the case of AFR antennas, beamforming is one of the key steps in the calculation of the beam patterns and hence, a study of different beamforming techniques is also presented. To support the results presented in this thesis, a benchmark with respect to available commercial software (GRASP from TICRA) is provided. Last, parametric studies at the system level are presented, which shows the potential of the rapid estimation of coverage characteristics in the design process of the satellite payload

Pressure profile prediction in building drainage system using artificial neural network (ANN) modelling

The mechanisms of fluid flow phenomena found in Building Drainage Systems (BDS) in the analysis of transient flow of air and water even though are grounded, needs attention with respect to its modelling, as it is quite relevant with respect to safe removal of the waste from the building and to avoid the trap seal depletion. This research seeks to predict the classical pressure profile for a range of BDS configurations using a number of available parameters such as pipe diameter, water discharge height, water discharge flow rate and overall building height using Artificial neural network (ANN) algorithms, optimised for BDS systems for the first time. Experimental data from peer reviewed literature and data from a unique 32- storey building drainage test rig have been used as pressure profile data (Target data) for an Artificial Neural Network (ANN) model. Discharge flow rate and height are considered to be the two independent input parameters, and the pressure along the vertical stack is considered to be the output parameter. In this work, both the Feed Forward Back Propagation (FFBP) ANN model and the Radial Basis Function (RBF) ANN model have been used to train, test, and validate the respective algorithms. Subsequently, a FF-PSO algorithm has been employed to reduce the intrinsic error in the Feed-Forward model by refining the weights and biases. The work has confirmed the applicability of all the tested models for steady two-phase fluid flow phenomena in BDS in different configurations. Further, the FFBP ANN model has been employed to establish a relationship among pressure profiles for the same discharge coming from different floors. The prediction of pressure profile of a BDS has also been modelled using the pressure profile of other BDS. In order to capture the change in characteristics of the data, a hybrid model with segregated data for dry-stack and wet-stack zone has also been employed. It is surmised that this model could be trained with a database of real-world system data in the future

Underwater imaging with single-photon avalanche diode detector arrays

The time-correlated single-photon counting (TCSPC) technique was introduced less than 30 years ago for acquiring range information and reconstructing three-dimensional scenes. Since then, the research field has undergone significant advancements in timing electronics, optical system configurations, image processing algorithms, and particularly single-photon detectors. The detectors used are typically semiconductor single-photon avalanche diode (SPAD) detectors, which can operate in the visible, near-infrared and short-wave infrared. More recently, single-photon imaging approaches have been used for high-depth resolution imaging in challenging environments where light scattering occurs, like through fog or underwater. This Thesis examines aspects of underwater single-photon imaging. Initially, a three-dimensional single-photon depth imaging system, was used for several experiments. A bistatic optical configuration was used with a picosecond pulsed laser system with an operational wavelength of 532 nm. In the receive channel a CMOS SPAD detector array with 192 × 128 pixels was used, with each pixel of the detector having its own time-to-digital converter for time-tagging detected events. This system was housed in a waterproof enclosure and submerged to a depth of approximately 1.8 m, recorded static target scenes at various scattering levels in water, with a target stand-off distance of 3 m. The three-dimensional imaging system demonstrated sub-centimetre depth resolution in clear water, and the angular resolution of the imaging system was evaluated to be 84 µrad (both horizontal and vertical). The experiment included the data acquisition of moving targets illustrating the capability of different algorithms in the real-time processing of single-photon data with low-latency in the presence of high levels of scattering. This work represented the first example of a fully submerged underwater single-photon imaging system. This bistatic transceiver system was used in the laboratory to examine the time-domain characterisation of scattering in water. A mathematical model was developed to reconstruct a single-event scattering process of photons in water. Consequently, this was used to establish the average scattering length in various underwater conditions. A different transceiver was constructed to utilise a linear 16 × 1 SPAD detector array, developed using a custom fabrication process. The use of the custom fabrication meant that low jitter performance was obtained across the detector array. This transceiver system recorded moving target scenes and was evaluated for high scattering levels, demonstrating its exceptional performance in highly scattering water. Its average optical illumination power was less than 15.5 mW with a depth resolution of better than 2.5 mm

Understanding CO2 flow measurement for carbon capture and storage (CCS) transport applications

Carbon Capture and Storage (CCS) is a decarbonization solution, particularly suited to industries with hard-to-abate emissions such as cement, iron & steel, and fertilizer production. However, as a prerequisite for commercialisation of CCS, accurate measurement is required for quantifying CO2 streams across the CCS value chain and to comply with a range of environmental legislation and regulations. Unlike other industrial process fluids such as water, oil, and natural gas, it is still unclear whether current commercially available metering technologies can meet the requisite accuracy levels, specifically the ±2.5% accuracy recommended within the EU/UK European Trading Scheme for CO2 mass transfer. Therefore, this research is aimed towards gaining a comprehensive understanding of flow measurement of CO2 under relevant CCS transport conditions. This understanding is crucial for examining the capabilities of both Coriolis and orifice meters under more realistic CCS transport conditions, specifically assessing whether these CCS metering technologies meet the MRR Tier 4 MPE requirement. The experimental study predominantly focuses on evaluating the performance of two distinct designs of Coriolis meters and an orifice meter, across gas, liquid, and supercritical conditions, using both pure CO2 and CO2-rich mixture samples. In order to understand the influence of non-condensable gas impurities in CCS flow operations, a review of relevant thermodynamic modelling equations was conducted. These models play a relevant role in predicting the optimal transport conditions for the CO2-rich mixtures. Moreover, a dedicated laboratory-scale gravimetric flow facility was designed for conducting CO2 flow measurement tests. Using this facility, flow measurement tests were conducted to evaluate the performance of the selected meters under gas, liquid, and supercritical flow conditions. Additional tests were conducted to assess the performance of one of the Coriolis meters with light energy carrier gases (hydrogen-methane blend). The findings from these flow experiments indicate that the non-condensable impurities, such as N2, H2, O2, Ar, and CH4 have a relatively minor impact on Coriolis meters, with maximum mean absolute errors of 0.51%, 0.26%, and 0.56% observed in gas, liquid, and supercritical CO2 flow conditions, respectively. However, the impact of these impurities, which is often associated with an increase in the compressibility of the fluid and reduction in density or homogeneity of the fluid, tends to become apparent with different Coriolis designs or quality of flow operation (flow rates and regions). In the case of the test orifice meters, impurities also have a less noticeable impact during gaseous flow conditions, with the highest recorded mean absolute error reaching approximately 1%. However, the impact of these impurities becomes more noticeable in liquid and supercritical flow conditions, resulting in maximum mean absolute errors of 2.84% and 11.14%, respectively. It is worth noting that although impurities seem to have a more pronounced effect in these dense phases (high density liquid and supercritical phases), a substantial component of these errors can be attributed to uncertainty in the density measurements. These results conclude that Coriolis metering technology as a robust choice for CCS metering, underscoring its suitability for accurate measurements in single phase CO2 transport conditions, as well as in handling other relevant low-carbon fluids. Meanwhile, the performance of orifice meters in gaseous flow conditions emphasizes their effectiveness and potential applicability in repurposed gas pipeline infrastructures for CCS transport applications. The overall outcome of this study helps contribute towards understanding flow measurement capabilities of specific commercially available CCS metering technologies. The assessment of these meters offers crucial insights and measurement data to understand how well some existing flow metering technologies, currently employed in the oil and gas industry, can be adapted for CCS transport metering applications. The study also helps understand the impacts of non-condensable gas impurities in CCS flow operations, showing how well these impacts can be handled to improve flow activities