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Outbound OI is informed by the idea that organisations should use alternative pathways to externalise their knowledge and commercialise their technologies. Ambidexterity suggests a high level of balance between opposite, competing and contrasting objectives such as exploration and exploitation, as well as radical and incremental innovation. The study focuses on outbound OI processes of technology creators and consumers in their current markets and the strong mutual interactions between the literature of outbound OI and ambidexterity. Innovation streams discuss patterns of innovation and are one way to mobilise exploration and exploitation in service and product development. The theory on which the thesis is developed views innovation as an evolutionary system. This vision is applied to the Apache Hadoop, the industry-standard ecosystem for the analysis of big data. Through an interpretive case study, the thesis investigates outbound OI processes in four case studies in six industry sectors in 25 organisations in Brazil. The work contributes to the research on ambidexterity in outbound OI processes in technology creators and consumers in their current markets. It suggests an interrelationship between the code base of the Apache Hadoop distributions (community and enterprise) and the way firms may innovate (discontinuous, architectural and incremental). Additionally, the thesis adds additional case studies on outbound OI processes in digital service platforms.

This thesis revolves around the debates surrounding the contextual turn in career research. Focusing on the careers of academic women at State University Business School in Finland and University College Business School in England, this research explores the conceptualisation of career agency in research analysis. Building on the tenets of practice-based studies, this research proposes a conceptual framework that explores how the context and conditions of career agency emerge at the intersection of the organisation of academic work, the expectations placed on academic work, and the locally shared practical understanding of femininity and masculinity. The analysis draws on the concept pair of authority and career capital. While authority directs attention to the organisation of academic work by exploring how authority places academics in certain relationships with each other and their activities based on a certain legitimation, career capital revolves around the expectations placed on academic work and how engagement in academic work results in cultural, social, and economic career capital. Gender, meanwhile, is assessed by exploring how the locally shared practical understanding of femininity and masculinity intertwines with the context and conditions of career agency. The findings suggest that while femininity is described as an active stance at State University Business School, University College Business School is characterised by a feminine–masculine dichotomy, in which the competitive and individualistic formulation of masculinity is perceived as detrimental to inclusion. However, it is not gender per se that causes divisions amongst academics. In fact, those who engage in academic work that generates income for the community might not be able to accumulate the kind of career capital that is required for promotions or recruitments, especially since the expectations stemming from managerial authority seem to underpin what is expected from an employable or promotable academic. Thus, the empirical analysis in this thesis highlights how the conditions of career agency emerge in a certain organisational setting, and shows how career agency can be addressed within career studies while retaining a gender perspective.

This thesis investigated the palaeoceanographic history of the Faeroe Shetland Basin, NE Atlantic, via identification and analysis of contourite drift deposits using petroleum industry seismic and well data. Integration of regional 2D seismic lines, 3D seismic volumes and high resolution seismic profiles w ith industrial well data permitted full spatial and temporal characterisation of the contourites within the basin, including identification of small scale architectural elements and subtle stratigraphic relationships. In turn, it was possible to make interpretations regarding the palaeoceanographic regime within the basin from the onset and evolution of thermohaline current flow through the basin to correlation with the present day oceanographic situation. Overall, the study serves to highlight the efficacy of industrial seismic and well data for contourite and palaeoceanographic research. A variety of contourite drift types were identified during seismic-chronostratigraphic division of the Cenozoic succession. Identification of an early middle Eocene contourite drift within the southern Faeroe Shetland Basin dates the onset of southerly flowing deep waters from the Norwegian Greenland Sea into the North Atlantic as part of a North Atlantic Conveyor Belt-style circulation system at approximately 49 million years, predating previous estimates by more than 15 million years. The presence of Oligocene, Miocene and Pliocene contourite drifts within the basin reveals that following initiation in the middle Eocene, southerly flowing deep water circulation through the basin was continuous throughout the late Palaeocene and Neogene to the present day. A gradual increase in deep water flux through the basin throughout this time is hypothesised based on contourite distribution, and is thought to relate to the global greenhouse to icehouse climatic transition that occurred during the Cenozoic. Pulses of increased deep water current velocity related to a combination of climatic and tectonic factors are thought to be responsible for the formation of major deep w ater erosional unconformities which are associated with the contourite drift successions. A link between contourite deposition and the climatic fluctuations that characterised the late Neogene northern hemisphere is also proposed based on the identification of direct indicators of significant glaciation including iceberg plough marks within the contourite drift units. Contourite drift deposition is interpreted to occur predominantly during climatically warmer episodes, while periods of glaciation are characterised by apparent disruption of ocean current circulation within the basin.

CO₂ has been seen as a green-house gas mainly responsible for the global climate change. According to National Oceanic and Atmospheric Administration (NOAA), CO₂ emission since 1980 contributes to half of the CO₂ increases in the last 300 years and 25% of the CO₂ concentration rise is attributed to the emission since 2000. The CO₂ concentration is increasing faster than ever now. Instant and effective actions are urgently required to curb the trend in order to constrain the global temperature increase lower than 1.5 °C. Besides CO₂ capture and storage, CO₂ catalytic conversion to value-added chemicals is another important strategy to reduce its concentration in the atmosphere. Solar light is one of the massive and widespread sustainable energy sources on the Earth. Using solar energy to drive CO₂ reduction is a promising and green way to reduce CO₂ concentration in atmosphere. The main challenges in practical implementation of the CO₂ conversion in large-scale are the relatively slow reaction rates and poor selectivity to more valuable products. The key to address these issues relies on better understanding of the reaction and photoexcitation mechanisms. This thesis focuses on building the correlation between the microstructure of catalysts, light induced excitation, reaction pathway, product selectivity and reaction rate. The research work presented in this thesis starts from investigating the CO₂ photocatalytic reduction with H₂O over g-C₃N₄ (Chapter 3). A comprehensive optimisation of molecular and electronic structure of pristine g-C₃N₄₄ is conducted. The designed modification of co-doped K, B and N Vacancy (NV) enhances light absorption, excited-electron/hole separation, CO₂ adsorption, therefore the overall significantly increases the photocatalytic CO and CH₄ production rate by 527% and 161% comparing with the pristine g-C₃N₄. The reaction mechanism has been investigated via in-situ DRIFTS. An important correlation is found between activated CO₂* concentration on catalyst surface and reaction rate. The enhanced capability to activate the adsorbed CO₂* is the key guidance for future photocatalysts design. Most of the current CO₂ photocatalytic reduction with H₂O can only produce CH₄ and CO. Therefore, Chapter 4 focuses on how to promote higher hydrocarbon production via CO₂ photocatalytic reduction with H₂O over Au/TiO₂-x with abundant oxygen vacancies (VO). The reaction performances are compared under UV and green light irradiation conditions. The UV light-driven CO₂ reduction shows primary product of CO; by comparison, the green light-driven CO₂ reduction produces 20% C₂H₆ and 80% CH₄. The electronic structure and microstructure of Au/TiO₂₂-x are investigated by a series of in-situ spectroscopy methods. The involvement of VO is found indispensable for this reaction. The different product selectivity under ultraviolet (UV) or green light irradiation is derived from the different excitation mechanism. The UV light bandgap excitation induces negatively charged Au that retards CO* adsorption; on the contrary, the green light plasmonic excitation causes positively charged Au that stabilises CO* adsorption. The stability of CO* key reaction intermediate is essential for higher hydrocarbon production. The two chapters mentioned above have demonstrated the feasibility to significantly improve the photocatalytic CO₂ reduction rate by optimising the catalysts. However, the photocatalytic reaction rate is still far from comparable with traditional thermocatalytic CO₂ reduction. The H₂O has been proven as a less effective proton donor in Chapter 4 and the role of H₂O needs further investigations. Therefore, in the Chapter 5 & 6, CO₂ hydrogenation to CO, known as the reversed water gas shift reaction (RWGS reaction), is investigated under photo-thermo coupled catalytic reaction condition. The external light promoted thermocatalytic RWGS reaction performances are compared over two typical catalysts: Au/TiO₂ and Au/γ-Al₂O₃. The reducible TiO₂ and non-reducible γ-Al₂O₃ supports are fundamentally different on whether the plasmonic hot electrons transfer to the support. The CO₂ reduction rates are found significantly higher over Au/TiO₂ than Au/Al₂O₃ on either pure thermocatalytic or photo-thermo coupled catalytic reaction conditions. Moreover, the plasmonic enhancement efficiency is also superior on Au/TiO₂ over Au/A₂O₃. In Chapter 5 & 6, the RWGS reaction mechanism in dark and photo-thermo coupled conditions are investigated via a series of in-situ spectroscopy and isotope kinetic analyses. The plasmonic enhancement mechanisms are also investigated via both experimental and theoretical approaches. The reaction mechanisms are proven to be quite different over Au/TiO₂ and Au/Al₂O₃. RWGS reaction follows a redox pathway with CO₂ self-dissociation at the VO site of Au/TiO₂. By comparison, the RWGS reaction mechanism over Au/Al₂O₃ is a mixture of formate and carboxyl reaction pathways. Accordingly, the plasmonic enhancement mechanisms are different too. In the case of Au/TiO₂, plasmonic hot electrons mainly facilitate the VO generation; while on Au/Al₂O₃, the formate pathway, especially the formate in bridged configuration, is preferentially promoted by plasmonic excitations. Overall, this thesis has expanded and deepened the understandings of the photocatalytic and photo-thermo coupled catalytic CO₂ reductions. The mechanisms elucidated in this thesis can inspire future works on designing and developing more efficient and economically-feasible CO₂ utilisation to address climate change challenge.

Feature extraction is an extremely important pre-processing step to pattern recognition, and machine learning problems. This thesis highlights how one can best extract features from the data in an exhaustively online and purely adaptive manner. The solution to this problem is given for both labeled and unlabeled datasets, by presenting a number of novel on-line learning approaches. Specifically, the differential equation method for solving the generalized eigenvalue problem is used to derive a number of novel machine learning and feature extraction algorithms. The incremental eigen-solution method is used to derive a novel incremental extension of linear discriminant analysis (LDA). Further the proposed incremental version is combined with extreme learning machine (ELM) in which the ELM is used as a preprocessor before learning. In this first key contribution, the dynamic random expansion characteristic of ELM is combined with the proposed incremental LDA technique, and shown to offer a significant improvement in maximizing the discrimination between points in two different classes, while minimizing the distance within each class, in comparison with other standard state-of-the-art incremental and batch techniques. In the second contribution, the differential equation method for solving the generalized eigenvalue problem is used to derive a novel state-of-the-art purely incremental version of slow feature analysis (SLA) algorithm, termed the generalized eigenvalue based slow feature analysis (GENEIGSFA) technique. Further the time series expansion of echo state network (ESN) and radial basis functions (EBF) are used as a pre-processor before learning. In addition, the higher order derivatives are used as a smoothing constraint in the output signal. Finally, an online extension of the generalized eigenvalue problem, derived from James Stone’s criterion, is tested, evaluated and compared with the standard batch version of the slow feature analysis technique, to demonstrate its comparative effectiveness. In the third contribution, light-weight extensions of the statistical technique known as canonical correlation analysis (CCA) for both twinned and multiple data streams, are derived by using the same existing method of solving the generalized eigenvalue problem. Further the proposed method is enhanced by maximizing the covariance between data streams while simultaneously maximizing the rate of change of variances within each data stream. A recurrent set of connections used by ESN are used as a pre-processor between the inputs and the canonical projections in order to capture shared temporal information in two or more data streams. A solution to the problem of identifying a low dimensional manifold on a high dimensional dataspace is then presented in an incremental and adaptive manner. Finally, an online locally optimized extension of Laplacian Eigenmaps is derived termed the generalized incremental laplacian eigenmaps technique (GENILE). Apart from exploiting the benefit of the incremental nature of the proposed manifold based dimensionality reduction technique, most of the time the projections produced by this method are shown to produce a better classification accuracy in comparison with standard batch versions of these techniques - on both artificial and real datasets.

The work described in this thesis focuses on the effects that adsorption processes on catalytic surfaces pose in controlling key steps that can affect and control reaction pathways. To that, the development of Nuclear Magnetic Resonance (NMR) relaxometry methods and the comparison with traditional catalytic was performed with a series of C5 and C6 unsaturated hydrocarbons on two different alumina supports, γ- and θ-Al2O3. The developed techniques were applied in the study of liquid phase selective hydrogenation of citral on 5% Pt/SiO2. Infrared (IR) spectroscopy, volumetric adsorption isotherms, dynamic isotherms via a Tapered Element Oscillating Microbalance (TEOM), temperature programmed desorption (TPD) as well as 13C T1 NMR and 1H 2D T1-T2 relaxometry methods were employed. Energies of adsorption as a function of coverage were obtained via adsorption isotherms and the particular surface adsorbate interactions were described with IR spectroscopy. For example, 1-pentyne showed the strongest interaction with the alumina (94 kJ mol-1) while 1-pentene presented a weaker interaction (46 kJ mol-1) on θ-Al2O3. Desorption energies obtained from TPD ranged 85 – 130 kJ mol-1, irrespective of the adsorbate. Reactivity of the aluminas was captured with TPD, TEOM and NMR relaxometry. Interaction of adsorbates with hydrocarbon occurred predominantly on weak adsorption sites. 13C NMR T1 relaxometry provided in addition atom-specific adsorbate-adsorbent interaction strengths, showing the molecular geometry of adsorption, and applied in co adsorption measurements. The selective hydrogenation of citral as a model α,β-unsaturated aldehyde and the effect of different solvents on the activity and product distribution was studied at 298 and 373 K. A series of polar protic, polar aprotic and non polar solvents was investigated. Results showed higher initial reaction rates in non polar solvents but higher selectivities towards desired products on polar protic solvents. Solvent used also affected by product formation. The strong variations in reaction rates and selectivities reported were related with adsorbate catalyst interactions, as well as solvent reactant interactions. For example, adsorption isotherms showed that ethanol notably reduced the adsorption capacity of citral as compared with hexane, related with the rate differences observed. ATR-IR measurements indicated solvent citral interactions were solely present in polar protic solvents in line with higher yields of geraniol and nerol. Finally, 13C T1 NMR and 1H 2D T1-T2 correlation experiments determined that the geometry of adsorption of citral, influenced by solvent, affected product selectivity, and that product adsorption affected selectivity and deactivation.

Extended X-ray Absorption Fine Structure (EXAFS), Energy Dispersive EXAFS (LDE), Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) and Temperature Programmed Desorption (TPD) have been employed to investigate the surface organometallic chemistry of rhodium and palladium supported on high area alumina and titania surfaces. All the supported organometallic systems in this study have been prepared by Metallo-Organic Chemical Vapour Deposition (MOCVD). Metal loadings of between 2% and 5% were used. DRIFTS and EDE data suggest that the [Rh(CO)₂Cl]₂ dimer is chemisorbed onto both the TiO₂ and Al₂O₃ surfaces as a [O]Rh(CO)₂(Cl) unit. DRIFTS, EDE and TPD studies show that the carbonyl ligands are removed by heating the system under an inert atmosphere up to 220°C, or under a hydrogen atmosphere up to 90°C. This results in the formation of small rhodium clusters on the oxide surface, shown by EDE spectroscopy. The gem-dicarbonyl species can be regenerated by exposure of the thermolysed sample to CO at room temperature. Reaction with NO results in substitution with the CO ligands to form a [O]₂Rh(NO)⁻(Cl) species which exhibits greater thermal stability than the gem-dicarbonyl species, requiring temperatures of 300°C to remove the ligands and cluster the rhodium. The pre-treatment of the titania and alumina surfaces has been shown to affect the nature of nitrosyl species generated. The gem-dicarbonyl species is regenerated by exposure to CO. Rh(CO)₂(acac) has been shown to be physisorbed intact on the surfaces of titania and alumina. Thermolysis under helium and hydrogen results in evolution of firstly the carbonyl and then the acac ligands. Regeneration of the gem- dicarbonyl ligands occurs with exposure to CO at room temperature. Reaction of NO results in the formation of a Rh(NO)⁻ species, with gem-dicarbonyl regeneration occurring after exposure at CO.

Class Imbalance is one of the numerous problems that affect the performance of a classifier. Data is said to be imbalanced when one or more classes have much fewer instances compared to other classes. In the process of learning from data, having a low number of instances of one class, results in a poor model. The Model tends to be partial to the class having a large number of instances. The issue of Imbalanced data sets is always ongoing research and there are several solutions to cope up with it in the case of two-class classification problems. However, for the multi-class imbalanced data, not every traditional technique helps to solve the problem. In the case of multi-class imbalanced data, few classes can have an extremely low number of instances and few can be moderately low. Balancing the distribution in such cases is a challenge. It is important to make sure that a model can differentiate between all available classes in the data.Apart from imbalanced data, Concept drifts also make a difference in learning a model. Concept Drifts are very likely to occur. For instance, a model that is trained on summer shopping trends can no longer be suitable to predict winter shopping trends. This paper is an analysis of how a combination of existing solutions for the above-mentioned problems behave on few synthetic and real-world data.

Analysis of scattered, or diffraction energy (the seismic response of small-scale objects) in the seismic data from Canterbury Basin, New Zealand reveals additional geological information about depositional patterns in sedimentary deposits. Diffrac- tion images from the seismic response for Canterbury Basin provide complementary interpretation tools to the conventional specular reflection images. To image diffrac- tions for a dataset from Canterbury Basin, I take the following steps: First, I attenuate multiples using a surface multiple prediction algorithm to predict multiples and apply regularized nonstationary regression to adaptively subtract the predicted multiples. Next, I separate diffractions using the plane-wave destruction method. The plane- wave destruction method removes conventional reflected energy in order to enhance the diffracted energy. I then apply a velocity continuation method on diffraction data to estimate migration velocities and then migrate the data using Kirchhoff migration in the dip-angle-gather domain. The resultant conventional and diffraction images are improved images suitable for geological interpretation of prograding sediment drifts.

A contourite drift complex on the J-Anomaly Ridge (JAR) and Southeast Newfoundland Ridge (SENR), offshore eastern Canada, records an extensive archive of North Atlantic circulatory and sedimentary dynamics formed under the influence of the Deep Western Boundary Current (DWBC). Seismic-reflection profiles constrained by drill sites from IODP Expedition 342 are used to map the spatial and temporal distribution of contourite sedimentation and to evaluate the Cenozoic history of the DWBC within a preexisting climatic framework. This study indicates three phases of sedimentation termed here Pre-Contourite-Drift Phase (~115-50 Ma), Active-Contourite-Drift Phase (~50-2.6 Ma), and Post-Contourite-Drift Phase (~2.6-0 Ma). Bottom current controlled sedimentation began at the boundary between Pre-Contourite-Drift Phase and Active-Contourite-Drift Phase (~50 Ma), and correlates to a long-term global cooling trend that initiated at the end of the Early Eocene Climatic Optimum. Within the Active-Contourite-Drift Phase at ~30 Ma depocenters shifted deeper and current energy and focus is interpreted to have increased in association with global oceanographic change at the Eocene-Oligocene transition. The beginning of Post-Contourite-Drift Phase sedimentation (~2.6 Ma) marks a shift in bottom current path towards shallower water depths, and corresponds with the onset of Northern Hemisphere ice sheets. These events of circulatory reorganization correlate with other North Atlantic seismic stratigraphic studies, suggesting that these events occurred throughout the North Atlantic. An improved understanding of long-term (>1000000 yr) dynamics of North Atlantic circulation in response to significant reorganization of Cenozoic climate provides important context towards refining models and prediction of oceanic response to contemporary climate change.

Eurocode-8 aims to protect human life from a design-seismic event and damage restriction for a frequent seismic event. However, it doesn't restrict the residual deformations and doesn't consider confining the damage accumulation due to possible aftershocks. Therefore, traditional seismic-resistant frames undergo large residual deformations that may increase further due to aftershock sequences as the energy is dissipated through inelastic deformations in the primary framing elements. This thesis proposes a novel seismic-resistant steel frame that maintains building functionality after significant earthquakes and subsequent events by reducing the collapse probability, residual deformations, and damage accumulation while theoretically eliminating impractical repairs by concentrating the damage in replaceable elements. The frame has energy-dissipating chevron-type braces equipped with replaceable hourglass-shaped pins made of duplex stainless steel. Under design seismic loading, energy is dissipated through inelastic deformations confined in the replaceable pins while the other framing elements remain elastic. As a result of the inherent properties of the stainless-steel pins, the frame exhibits high post-yield stiffness, which reduces the residual drifts. Moreover, the system can hypothetically restore its normal seismic performance after replacing the damaged pins if the damage is confined in the pins and residual deformations are kept below the construction tolerance limits. The seismic performance objectives are set to prevent global collapse under a rare seismic event and secure human life under a design-seismic action. Furthermore, a target is established to offer a resilient seismic performance by eliminating the damage in the framing elements under a design-seismic event. Then, additional damage limitation objectives are set to limit peak and residual displacements under frequent and design-level earthquakes, respectively. Last, an objective is set so that the seismic performance of the frame is not curtailed due to aftershock sequences while limiting the damage accumulation in the pins. A novel Eurocode-8-based design methodology is developed for concentrically-braced systems with high post-yield stiffness and adopted for various archetypes. Then, the risk-consistent approach recently developed by (Vamvatsikos et al., 2020) (INNOSEIS) is adopted to determine Eurocode-8-compatible seismic behaviour and overstrength factors. Specifically, the behaviour factor is assessed through fragility analysis based on incremental dynamic analysis considering high and medium seismicity site-specific ground motion suites; each matches the seismic hazard at three European sites. In contrast, the overstrength factor is evaluated through nonlinear pushover analysis. Furthermore, the recently developed mainshock-consistent-aftershock sequences selection procedure developed by (Papadopoulos et al., 2020)is integrated with the INNOSEIS approach in a novel way to evaluate the effects of earthquake sequences effects on the seismic performance of the frame. Here, the behaviour factor is evaluated by adopting site hazard-specific sequences selected for a site in Terni, Central Italy, through damage-dependant fragility analyses based on nonlinear back-to-back dynamic analyses at multiple intensity levels. Afterwards, prediction models for the damage accumulation in the pins are developed by linear regression while employing cumulative energy or duration-based intensity measures. Then, the behaviour factor is evaluated in terms of damage accumulation using the most reliable prediction model. Therefore, a detailed numerical nonlinear model is constructed in OpenSees for the proposed frame integrating experimentally calibrated modelling features. The model incorporates geometrical and material nonlinearities capturing the strength and stiffness deterioration of the primary framing members while modelling the fracture of the pin-brace system. The detailed numerical model and the developed design methodology can be adopted for comparable systems with high post-yield stiffness, conceptually pinned connections, and possessing symmetric behaviour. It is validated that the seismic performance of the proposed frame, adopting a behaviour factor of 6.5 and a design overstrength factor of 3, prevents global collapse and assures life safety. Also, it ensures concentrating the damage in the pins under a design-seismic event. Moreover, it guarantees to keep the residual drifts below 1/300 under a design-seismic action and the peak drifts below 0.75% under a frequent seismic event. However, a reduced behaviour factor of 4 is recommended to keep the residual drifts below 1/500 and, for archetypes up to 12-storey, the peak drifts below 0.5%. Last, The performance is validated under mainshock-aftershock sequences for archetypes up to 12-storey, confirming that a behaviour factor of 6.5 guarantees to keep the damage accumulation index of the pins below 30%, demonstrating the superior fatigue capacity of the system under earthquake sequences. Here, the cumulative absolute velocity-based model is adopted as it efficiently predicts the damage accumulation index.

In the aftermath of the 1994 Northridge earthquake, many steel Moment Resisting Frames (MRFs) were revealed to have severe deficiencies that compromised their future seismic performance. Since then, many seismic design codes have rapidly evolved to address these deficiencies; nonetheless, several existing steel frames were built before the introduction of modern seismic design codes and are in need of reliable assessment methodologies which include provisions on the modelling and evaluation of structural components that are often overlooked, such as panel zones, and non-structural components that may have a contribution on the strength and stiffness of the structure, such as masonry infills. Many modern assessment codes also include recommendations for the strengthening of deficient buildings. For the retrofitting of steel MRFs, Buckling-Restrained Braces (BRBs) represent an advantageous solution, as these devices can provide strength, stiffness and energy dissipation capacity. Nonetheless, the European assessment regulation lacks guidelines for these devices, while the USA regulations include design procedures which tend to focus only on a few characteristics of the BRBs, neglecting aspects such as the residual drifts induced by the low post-yielding stiffness or their propensity for low-cycle fatigue rupture as a consequence of cumulative ductility, particularly in multiple earthquake scenarios. Thus, the present thesis focuses on addressing some of these deficiencies and omissions in assessing steel MRFs and retrofitting these structures by means of BRBs. The document starts with a comprehensive critical comparison between modern assessment codes to establish the limitations of the current assessment regulations. Then, it focuses on the strengthening aspects by reflecting on the impact of the design decisions on the performance of the BRB-retrofitted structure and provides a probabilistic framework to evaluate the path-dependent failure modes of the BRBs, including the cumulative ductility and residual drifts. In addition to the analytical and numerical work, the present thesis uses experimental data to calibrate a masonry strut model that allows evaluation of the impact of these elements on the steel frame's performance and their interaction with the BRB retrofitting system.

Blood is one of the most common biological fluids encountered at crime scenes and is therefore constantly being tested for in the laboratory. Confirming the presence of blood can illuminate essential elements of a case as well as allow for identification via downstream DNA analysis. This significant investigative value is why it is crucial to use robust forensic testing techniques for blood detection. In the forensic laboratory, blood is identified using serological techniques. A presumptive test, such as a colorimetric test, is performed first. A confirmatory test, such as an immunochromatographic assay, is often performed following a presumptive positive result. While both types of tests have numerous advantages, they have several limitations as well. These limitations have served as the basis for exploring alternative techniques for forensic blood detection, such as FTIR. FTIR spectroscopy is a qualitative, non-destructive, confirmatory analytical technique. This technique uses infrared light to characterize organic compounds based on molecular structure. There are also several different FTIR techniques, such as ATR and DRIFTS. ATR-FTIR analysis has been widely researched for the detection of blood and other biological fluids, across several applications. ATR-FTIR may be preferable to serological blood detection because it can be quicker than combined serological blood testing, it requires minimal sample preparation, it does not damage DNA downstream, and it can detect multiple biological fluids at once. Despite all the advantages that ATR-FTIR analysis has over traditional forensic blood techniques, it has not yet been implemented in casework. This may be due to skepticism in using subjective and complex spectroscopic data that results from ATR-FTIR analysis of body fluids. The initial objective of this research was to develop an optimized protocol using ATR-FTIR and chemometric analysis to identify blood on cotton round substrates. Using these techniques together would allow for a rapid, nondestructive, confirmatory approach, that would be more objective than serological testing or FTIR analysis alone. However, due to complications throughout the research process, this objective was altered. The revised objective was to develop an optimized protocol using DRIFTS and chemometric analysis to identify blood on cotton round substrates. An optimized DRIFTS protocol for forensic blood identification was successfully developed. Blood samples from multiple donors were tested using this protocol, and all samples showed similar data. Human biological samples other than blood as well as non-human samples were also tested. These samples showed dissimilar data from the donors’ blood sample data. Chemometric analysis was then performed using AnalyzeIQ Lab software. After testing 93 pair-wise combinations of pre-processing methods and algorithms, a model was developed. Unfortunately, this model was not completely optimized. It had a 9.09% error rate, resulting from the misclassification of one sample. Future research is needed before implementation into casework. Alternative cotton substrates and data collection software should be considered. Additional time should be spent using AnalyzeIQ Lab software, to develop a model with a 0% error rate. If this cannot be achieved, an alternative chemometric analysis software should be considered.

Abstract: Research on cultural products suggest that there are substantial cultural variations between East Asian and Western landscape masterpieces from the 15th to 19th centuries and contemporary members’ landscape artwork (Masuda, Gonzalez, Kwan, & Nisbett, 2008), and that these cultural differences in drawing styles emerge around the age of 8 (Senzaki, Masuda, & Nand, 2014). However, culture is not static. To explore the dynamics of cultural change, research should be conducted from both historical and ontogenetic perspectives (Tomasello, 1999; Vygotsky, 1978). In this thesis, I examined (1) 17th to 20th century Japanese and Western landscape masterpieces, and (2) adolescent data in landscape artworks alongside findings from Senzaki et al.’s (2014) research on elementary school-aged children. The results demonstrated cultural variations in artworks and masterpieces, and “cultural drifts” (Herskovits, 1941; 1948) where at certain time periods cultural tendencies deviated from default cultural patterns but occasionally returned to its previous state. The bidirectional influence of culture and implications for furthering the discipline of cultural psychology will be discussed.

Single-atom and cluster catalysts have been receiving significant interest due to not only their capability to approach the limit of atom efficiency but also to explore fundamentally unique properties. Supported Pt-group single atoms and clusters catalysts in the subnanometer size regime maximize the metal utilization and were reported to have extraordinary activities and/or selectivities compared with nanoparticles for various reactions including hydrogenation reactions. However, the relationship between metal nuclearity, electronic and their unique catalytic properties are still unclear. Thus, it is crucial to establish their relations for better future catalyst design. Ethylene hydrogenation and acetylene hydrogenation are two important probe reactions with the simplest alkene and alkyne, and they have been broadly studied as the benchmark reactions on the various catalyst systems. However, the catalytic properties and reaction mechanism of those hydrogenation reactions for metal nuclearitiy in the subnanometer regime is still not well understood. In this study, we applied different characterization techniques including x-ray absorption fine structure (XAFS), X-ray diffraction (XRD), X-ray photoelectron spectroscopy(XPS), diffuse reflectance infrared spectroscopy (DRIFTS), calorimetry and high-resolution scanning transmission electron microscopy (STEM) to investigate the structure of Pt/TiO2 and Pd/COF single-atom catalysts and tested their catalytic properties for hydrogenation reactions. In order to develop such relations, we varied the nuclearity of Pt supported on TiO2 from single atoms to subnanometer clusters to larger nanoparticles. For acetylene hydrogenation, Pt in the subnanometer size regime exhibits remarkably high selectivity to ethylene compared to its nanoparticle counterparts. The high selectivity is resulted from the decreased electron density on Pt and destabilization of C2H4, which were rationalized by X-ray photoelectron spectroscopy and calorimetry results. On the other hand, the activity of H2 activation and acetylene hydrogenation decreased as Pt nuclearity decreased. Therefore, our results show there's a trade-off between activity and selectivity for acetylene hydrogenation. Additionally, the kinetics measurements of ethylene hydrogenation and acetylene hydrogenation were performed on Pt/TiO2 catalysts, and they found to be structure sensitive for both reactions, which the reaction orders and activation energy changes as particles size change. The activity of ethylene hydrogenation decreases, and activation energy increase from 43 to 86 kJ/mol, as Pt nuclearity decreased from an average size of 2.1 nm to 0.7 nm and single atoms. The reaction orders in hydrocarbons (ethylene and acetylene) were less negative on subnanometer clusters and single atoms in contract to nanoparticles. The results imply that hydrocarbons, ethylene and acetylene species, do not poison the catalyst on Pt in the subnanometer size regime, and hydrogen activation turn to competitive adsorption path with surface hydrocarbons species. Moreover, single atom Pd supported on imine-linked covalent organic framework was synthesized, characterized by a various of techniques including X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) of adsorbed CO, and evaluated its catalytic properties for ethylene hydrogenation. The XAS results show that Pd atoms are isolated and stabilized by two covalent Pd–N and Pd-Cl bonds. DRIFTS of CO adsorption shows a sharp symmetrical peak at 2130 cm−1. The Pd single atoms are active for hydrogenation of ethylene to ethane at room temperature. The reaction orders in C2H4 and H2 were 0.0 and 0.5 suggesting that ethylene adsorption is not limiting while hydrogen forms on Pd through dissociative adsorption.

To meet increasingly stringent automotive emissions standards, further improvements in catalytic converter design are necessary. Current automotive catalyst systems are effective at eliminating emission of nitrogen oxides (NOx) once the catalyst reaches operational temperature (~200 °C). NOx emitted at lower catalyst temperatures now comprises most of the NOx released during a typical test cycle. Referred to as “the cold start problem” this issue has come to the forefront of automotive catalyst development, as mitigating these emissions is necessary to further reduce automotive emissions. Passive NOx adsorbers present an appealing solution to the cold start problem, these being a class of materials that chemisorb exhaust components such as NOx, carbon monoxide (CO) and hydrocarbons at near-ambient temperatures, and then desorb these compounds once the downstream catalyst has reached operational temperature. An effective passive NOx adsorber must have several properties: high NOx adsorption at near-ambient temperatures, near-complete NOx desorption at temperatures within the operational range, high thermal stability, and resistance to automotive exhaust components at high temperatures. The potential environmental impact of such a system is substantial, as NOx emissions currently result in the formation of millions of tons of smog and acid rain each year. Pd-exchanged zeolites have shown promise for deployment as Passive NOx adsorbers, though much remains to be understood about their adsorption chemistry and deactivation. In-situ Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) provides a convenient probe of adsorbed species, most automotive exhaust components possessing IR-active chemical bonds. By examining the evolution of IR bands under various pretreatments and adsorbates, the overall Pd-speciation and adsorptive zeolite sites of each material can be characterized, and the identities of IR bands can be deduced. In this work, microreactor-MS analysis of the adsorption and desorption behavior of these materials was also examined, these results being coupled with in-situ DRIFTS temperature programmed desorption (TPD) to correlate desorption events with specific adsorbed species. A pair of zeolite frameworks of similar Si/Al ratio but differing pore size were examined, Beta zeolite (BEA) and Chabazite (CHA) representing a medium- and small-pore framework, respectively. The effect of Pd-loading on BEA was examined, as well as the various deactivation pathways and active sites of each material.

Online data stream learning requires to process each training example upon arrival. Machine learning algorithms designed to process data in this manner are particularly suitable for this digital era, as the volume and the incoming speed of data are growing everyday. However, the environments of most real-world applications in online data stream learning are usually non-stationary, meaning that their underlying distribution may change over time (concept drift). Also, in real-world classification task, the number of examples from different classes are unlikely to be equal (class imbalance). Existing literature of concept drift adaptation and class imbalanced learning have been well-established in the last years. Yet, there still exists little work to address the joint challenge of them. This thesis contributes to this area of study by investigating how to deal with concept drift from a memory perspective and investigating how to create synthetic examples to aid in learning the skewed class while able to adapt to concept drift in class imbalanced data streams. The main contributions of this thesis are: - A study of the impact of different memory management strategies to the predictive performance. - A novel diversity-based memory management strategy that can maximise the chance of having relevant past knowledge to exploit in dealing with concept drift. - A novel framework called Concept Drift Handling Based on Clustering in the Model Space (CDCMS), which can deal with multiple types of concept drifts in class balanced data streams by exploiting relevant past knowledge. - A set of novel memory strategies for class imbalanced learning that can maintain and recover relevant past knowledge to deal with concept drift in class imbalanced data stream. From that, a novel framework called Concept Drift Handling Based on Clustering in the Model Space for Class Imbalanced Learning (CDCMS.CIL) is proposed. - A novel approach called Synthetic Minority Oversampling based on Stream Clustering (SMOClust), which creates synthetic examples based on stream clustering to aid in learning the skewed class, while able to adapt to concept drifts.

The Mozambique Channel locates in the Southwest Indian Ocean between the East African continental margin off Mozambique and Madagascar. On the Mozambican continental margin, apart from the settling of pelagic and hemipelagic particles, great thicknesses of terrigenous sediments have accumulated, being transported and deposited by three principal processes: mass-failures, turbidity currents, and contour currents. Despite this area represents an ideal setting to study these contour currents, their impact on sediments have been poorly studied. This Ph.D. work aims to develop our understanding of the mechanisms behind contour currents, controlling the formation of contourite depositional systems (CDSs) in the Mozambique Channel during the Cretaceous and Cenozoic. The broad data set available for this Ph.D. work includes high-resolution multibeam datasets, seismic data, hydrographic data, and results of a numerical model (a regional oceanic modeling system). Present-day CDSs of the Mozambique Channel are recognized from the upper continental slope to the abyssal plain. These CDSs show a broad array of morphologies, comprising plastered drifts, elongated-mounded drifts, sediment waves, contourite terraces, moats, contourite channels, abraded surfaces, scours, and furrows. Bottom currents and secondary oceanographic processes (internal waves) control the morphology of CDSs. Other predisposing factors for their morphologies identified in the Mozambique Channel are the presence of seafloor irregularities and the proximity/intensity of sediment sources. Hydrodynamic modeling of the present-day oceanic circulation showed that seafloor obstacles serve to focus the flow path of bottom currents while periods and areas with high rates of sedimentation superimposed on the whole CDSs, triggering mass-failures and turbidity currents. In the ancient record, the onset and burial phases of CDSs are mainly driven by tectonics (with the opening, shoaling, and closing of major oceanic gateways), leading to major palaeoceanographic changes. Locally, their variable morphologies confirm the present-day studies where seafloor morphology and proximity/intensity of sediment sources play a significant role. Specific conditions, where it exists a relative equilibrium between long-term steady bottom currents flow and short-term turbidity currents, might have economic significance for the petroleum industry, occurring more specifically along the contourite terraces.

The extensive growth of digital technologies has led to new challenges in terms of processing and distilling insights from data that generated continuously in real-time. To address this challenge, several data stream mining techniques, where each instance of data is typically processed once on its arrival (i.e. online), have been proposed. However, such techniques of-ten perform poorly over non-stationary data streams, where the distribution of data evolves over time in unforeseen ways. To ensure the predictive ability of a computational model working with evolving data, appropriate data-stream mining techniques capable of adapting to different types of concept drifts are required. So far, ensemble-based learning methods are among the most popular techniques employed for performing data stream classification tasks in the presence of concept drifts. In ensemble learning, multiple learners forming an ensemble are trained to obtain a better predictive performance compared to that of a single learner. This thesis aims to propose and investigate novel hybrid metaheuristic methods for per-forming classification tasks in non-stationary environments. In particular, the thesis offers the following three main contributions. First, it presents the Evolutionary Adaptation to Concept Drifts (EACD) method that uses two evolutionary algorithms, namely, Replicator Dynamics (RD) and Genetic algorithm (GA). According to this method, an ensemble of different classification types is created based on various feature sets (called subspaces) randomly drawn from the target data stream. These subspaces are allowed to grow or shrink based on their performance using RD, while their combinations are optimised using GA. As the second contribution, this thesis proposes the REplicator Dynamics & GENEtic (RED-GENE)algorithm. RED-GENE builds upon the EACD method and employs the same approach to creating different classification types and GA optimisation technique. At the same time, RED-GENE improves the EACD method by proposing three different modified versions of RD to accelerate the concept drift adaptation process. The third contribution of the thesis is the REplicator Dynamics & Particle Swarm Optimisation (RED-PSO) algorithm that is based on a three-layer architecture to produce classification types of different sizes. The selected feature combinations in all classification types are optimised using a non-canonical version of the Particle Swarm Optimisation (PSO) technique for each layer individually. An extensive set of experiments using both synthetic and real-world data streams proves the effectiveness of the three proposed methods along with their statistical significance to the state-of-the-art algorithms. The proposed methods in this dissertation are consequently compared with each other that proves each of the proposed methods has its strengths to-wards concept drift adaptation in non-stationary data stream classification. This has led us to formulate a list of suggestions on when to use each of the proposed methods with regards to different applications and environments.

The pedestal is a region of increased equilibrium pressure gradients near the edge of high performance toroidal plasmas. While the MHD properties of this region have been fairly well-characterized, pedestal microinstabilities and turbulence remain rela- tively unexplored, particularly in steep gradient regions. In this thesis, we describe a new microinstability caused by the steep equilibrium temperature gradients and com- plex magnetic geometry. This instability has a critical temperature gradient that is much higher than core temperature gradients, and hence likely exists only in pedestals. Basic analytic arguments show that in the presence of magnetic shear and steep temperature gradients, this mode must be one of the fastest growing modes. In realistic magnetic equilibria that we study, it is the fastest growing mode at almost all scales comparable to ion and electron gyroradii. Therefore, it is a robust feature of pedestal microinstability. We also investigate nonlinear pedestal microturbulence. We find the turbulent satu- rated state to be inhomogeneous in the poloidal angle, in strong contrast to core micro- turbulence that is typically well-correlated for long distances along magnetic field lines. Turbulence is particularly strong in poloidal regions of (i) weaker local magnetic shear and (ii) shorter distances between flux surfaces. These two effects cause the perpendicular wavenumber of the turbulence to have a particularly strong poloidal angle dependence, which results in the turbulence being strongly damped by finite Larmor radius effects in certain poloidal regions, hence causing the turbulent poloidal inhomogeneity. While we find that the linear instabilities caused by magnetic drifts are the fastest growing modes, nonlinear simulations appear to reach a quasi-steady state insensitive to these drifts and dominated by the branch of electron temperature gradient turbulence caused by parallel streaming. The heat flux in this quasi-steady state is roughly constant in time, but some modes that appear to be driven by magnetic drifts have not yet saturated.

Formaldehyde (HCHO) is one of the most common indoor air pollutants which causes adverse health effects, and a known carcinogen which has been proven to cause nasopharyngeal cancer and leukaemia, over prolonged exposure. Catalytic oxidation, amongst the known techniques for HCHO removal, is the most promising, as it is capable of completely mineralizing HCHO into CO2 and H2O. Although noble metal-based catalysts are very active for HCHO oxidation, their practical application is limited by cost and availability. Transition metal-based catalyst offers a promising cost-effective alternative. However, improving their catalytic performance is critical to enhancing their competitiveness for practical and industrial application in indoor HCHO abatement. Birnessite-type manganese oxide (δ-MnO2) is a promising transition metal catalyst for HCHO removal from the indoor environment. Nonetheless, not much has been reported in the enhancement of its catalytic activity. This research focused on developing novel strategies to enhance the catalytic activity of δ-MnO2 through surface defect engineering using doping technique; the use of oxygen carrier; and fine-tuning the concentration of the cations present in its interlayer spaces. The developed catalysts were characterized and evaluated for HCHO oxidation. The surface reaction mechanism of HCHO oxidation was investigated via in situ Diffuse Reflectance Infrared Fourier Transform (DRIFTS). Defect engineering through the creation of oxygen vacancies has been demonstrated to be an effective strategy for enhancing the catalytic activity of δ-MnO2 for HCHO. Modification of δ-MnO2 using Co and Cu via metallic doping revealed that doping technique is promising in enhancing the catalytic activity of δ-MnO2. However, catalytic and characterization results revealed that not all dopants enhance catalytic activity. While Co showed a synergistic effect, an inhibition effect was observed in the presence of Cu. The incorporation of Co3+ into the lattice structure of δ-MnO2 enhanced the substitution of Mn4+, generation of oxygen vacancies and lattice defects, which promoted the formation of surface-active oxygen species, and enhanced catalytic activity for low-temperature oxidation of HCHO. Catalytic results showed that Co-doped δ-MnO2 completely mineralize HCHO (~170ppm, 120,000 ml∙g-1∙h-1) at 80°C compared to 90°C for the pristine δ-MnO2. Co-doped δ-MnO2 proved very active even under a very high space velocity of 400,000 ml∙g-1∙h-1. Its activity was further displayed in its ability to achieve room temperature oxidation of HCHO, averaging up to 93.5% conversion of ~10 ppm HCHO (60,000 ml∙g-1∙∙h-1) over 72 hrs of operation. However, dopant-intermediates interaction can adversely affect the activity of the modified catalyst. Cu doping on the other hand led to a drastic inhibition of catalytic activity, despite the relative increase in the surface oxygen vacancies and concentration of surface active oxygen, compared to the pristine δ-MnO2. In the presence of Cu, the catalytic activity of δ-MnO2 was inhibited, and the inhibitory effect increases with increasing Cu doping. DRIFTS analysis revealed that in the presence of Cu, carbonate intermediate species accumulate on the surface of the catalysts leading to the restricted access to catalyst’s active sites and reduced catalytic activity. CeO2 was used to further enhance the catalytic activity of Co-doped δ-MnO2. As an oxygen carrier, the oxygen storage capacity of CeO2 and its ability to transfer its lattice oxygen, was utilized to achieve a bimetallic co-doping of Co and Ce into δ-MnO2. Bimetallic doping of Co and Ce (0.05 = x/Mn; x: Co, Ce) did not show any significant enhancement in the activity of δ-MnO2, compared to the monometallic doped catalysts – Co-δ-MnO2 and Ce-δ-MnO2, with similar doping ratio. Both the bimetallic doped (0.05Co-0.05Ce) and monometallic doped catalysts (0.05Co-MnO2 and 0.05Ce-MnO2) showed similar activity trend and achieved complete oxidation of HCHO at 80°C (~170ppm, , 120,000 ml.g-1.h-1). Complete oxidation shifts to higher reaction temperature of 90 and 100°C for 0.05Co-0.2Ce and 0.05Co-0.5Ce, respectively. As the amount of CeO2 loading increases from 0.05 to 0.5 (Ce/Mn ratio), significant loss in the interlayer K+ and decrease in catalytic activity was observed due to surface accumulation of CeO2 and structural collapse of δ-MnO2. In addition, comparatively lower content of active oxygen species was observed at high CeO2 loading. Besides, methanol was detected over all the CeO2 containing catalysts except 0.05Co-0.05Ce. However, in the absence of Co, even at low doping ratio of 0.05 of CeO2 (0.05Ce-MnO2) methanol was detected. Methanol selectivity increases with increasing CeO2 content. DRIFTS analysis shows that methoxy specie, which is a methanol intermediate, is generated in the presence of CeO2. As such, the presence of CeO2 in the catalyst matrices leads to the generation of a secondary pollutant. This work raises questions on the health safety of using CeO2 as a catalytic material for the abatement of indoor air pollutant. The interlayer space of δ-MnO2 is composed of ions, mostly K+ or Na+, and water molecules which help in stabilizing the layered structure of δ-MnO2. In Chapter 5, the role of K+ in lattice oxygen mobility of manganese oxide catalysts for enhanced catalytic oxidation of formaldehyde (HCHO) was investigated. Manganese oxide catalysts with varying K+ content and tuneable concentration of lattice oxygen and Mn4+ were synthesized and investigated for HCHO oxidation. Acid treatment of the pristine catalysts led to the creation of oxygen vacancies, reduction in the surface concentration of lattice oxygen, Mn4+ and K+, and a resultant increase in surface area. Characterization results showed that high content Mn4+ enhances the redox properties of the catalyst and K+ improves the mobility and activity of the lattice oxygen to re-oxidize the reduced Mn active sites and provide more surface oxygen for HCHO oxidation. Catalytic activity increases with increasing K+ content, and the surface concentration of lattice oxygen and Mn4+. The catalyst with the highest amount of K+, lattice oxygen, and Mn4+ displayed the best catalytic activity, achieving complete HCHO conversion (170 ppm, 45% relative humidity (RH), 60,000 ml·g-1·h-1) at 70°C, and an average of 68% HCHO conversion (10 ppm, 60,000 ml·g-1·h-1, 45% RH) at room temperature for 72 hrs. Lattice oxygen test, in the absence of molecular oxygen, revealed that the lattice oxygen is involved in HCHO oxidation and K+ enhances its mobility. Acid treatment increases the concentration of surface adsorbed oxygen leading to an initial improvement in catalytic activity, but the absence of K+ impacted the mobility of the lattice oxygen and the activation of molecular oxygen to supplement the consumed oxygen species, resulting into reduced catalytic activity.

This thesis divides neatly into four collections of results. In the first (Part II), we provide existence and uniqueness results along with several properties for a class of McKean-Vlasov Equations having random coefficients and drifts of superlinear growth. We show a Freidlin-Wentzell-type Large Deviations Principles (LDP) in the Hölder topology for the solution process of McKean-Vlasov Equations using techniques that directly address the presence of the law in the coefficients. Our methods avoid using decoupling tricks or particle system approximations. Secondly (Part III), we close an unexpected gap in the literature concerning the Malliavin and Parametric Differentiability of Stochastic Differential Equations with drifts of super linear growth and with random coefficients. We establish Stochastic Gâteaux Differentiability and Ray Absolute Continuity and take limits in probability rather than mean square or almost surely, bypassing the potentially non-integrable error terms from the unbounded drift. As an application, we recover representations linking both derivatives as well as a Bismut- Elworthy-Li formula. Thirdly (Part IV), we prove a representation for the support of McKean-Vlasov Equations. To do so, we construct functional quantizations for the law of Brownian motion as a measure over the (non-reflexive) Banach space of Hölder continuous paths. By solving optimal Karhunen Loève expansions and exploiting the compact embedding of Gaussian measures, we obtain a sequence of deterministic finite supported measures that converge to the law of a Brownian motion with explicit rate. We show the approximation sequence is near optimal with very favourable integrability properties and prove these approximations remain true when the paths are enhanced to rough paths. These results are of independent interest. The functional quantization results then yield a novel way to build deterministic, finite supported measures that approximate the law of the McKean-Vlasov Equation driven by the Brownian motion which crucially avoid the use of random empirical distributions. These are then used to solve an approximate skeleton process that characterises the support of the McKean-Vlasov Equation. We give explicit rates of convergence for the deterministic finite supported measures in rough path Hölder metrics and determine the size of the particle system required to accurately estimate the law of McKean-Vlasov equations with respect to the Hölder norm. Finally (Part V), we study the Small Ball Probabilities of Gaussian rough paths. While many works on rough paths study the Large Deviations Principles for stochastic processes driven by Gaussian rough paths, it is a noticeable gap in the literature that Small Ball Probabilities have not been extended to the rough path framework. LDPs provide macroscopic information about a measure for establishing Integrability type properties. Small Ball Probabilities provide microscopic information and are used to establish a locally accurate approximation for a random variable. Given the compactness of a Reproducing Kernel Hilbert space ball, its Metric Entropy provides invaluable information on how to approximate the law of a Gaussian rough path. As an application, we are able to find upper and lower bounds for the rate of convergence of an empirical rough Gaussian measure to its true law in pathspace.

Supported noble metal catalysts make the best utilization of noble metal atoms. Recent advances in nanotechnology have brought many attentions into the rational design of catalysts in the nanometer and subnanometer region. Recent studies showed that catalysts in the subnanometer regime could have extraordinary activity and selectivity. However, the structural performance relationships behind their unique catalytic performances are still unclear. To understand the effect of particle size and shape of noble metals, it is essential to understand the fundamental reaction mechanism. Single atoms catalysts and subnanometer clusters provide a unique opportunity for designing heterogeneous catalysts because of their unique geometric and electronic properties. CO oxidation is one of the important probe reactions. However, the reaction mechanism of noble single atoms is still unclear. Additionally, there is no agreement on whether the activity of supported single atoms is higher or lower than supported nanoparticles. In this study, we applied different operando techniques including x-ray absorption fine structure (XAFS), diffuse reflectance infrared spectroscopy (DRIFTS), with other characterization techniques including calorimetry and high-resolution scanning transmission electron microscopy (STEM) to investigate the active and stable structure of Ir/MgAl2O4 and Pt/CeO2 single-atom catalysts during CO oxidation. With all these characterization techniques, we also performed a kinetic study and first principle calculations to understand the reaction mechanism of single atoms for CO oxidation. For Ir single atoms catalysts, our results indicate that instead of poisoning by CO on Ir nanoparticles, Ir single atoms could adsorb more than one ligand, and the Ir(CO)(O) structure was identified as the most stable structure under reaction condition. Though one CO was strongly adsorbed during the entire reaction cycle, another CO could react with the surface adsorbed O* through an Eley-Rideal reaction mechanism. Ir single atoms also provide an interfacial site for the facile O2 activation between Ir and Al with a low barrier, and therefore O2 activation step is feasible even at room temperature. For Pt single-atom catalysts, our results showed that Pt(O)3(CO) structure is stable in O2 and N2 at 150 °C. However, when dosing CO at 150 °C, one surface O* in Pt(O)3(CO) could react with CO to form CO2, and the reacted O* can be refilled when flowing O2 again at 150 °C. This suggests that an adsorbed CO is present in the entire reaction cycle as a ligand, and another gas phase CO could react with surface O* to form CO2 during low-temperature CO oxidation. Supported single atoms synthesized with conventional methods usually consist of a mixture of single atoms and nanoparticles. It is important to quantify the surface site fraction of single atoms and nanoparticles when studying catalytic performances. Because of the unique reaction mechanism of Ir single atoms and Ir nanoparticles, we showed that kinetic measurements could be applied as a simple and direct method of quantifying surface site fractions. Our kinetic methods could also potentially be applied to quantifying other surface species when their kinetic behaviors are significantly different. We also benchmarked other in-situ and ex-situ methods of quantifying surface site fraction of single atoms and nanoparticles. To bridge the gap between single atoms and nanoparticles and have a better understanding of the effect of nuclearity on CO oxidation, we also studied supported Ir subnanometer clusters with the average size less than 0.7 nm (< 13 atoms) prepared by both inorganic precursor and organometallic complex Ir4(CO)12. Low-temperature CO adsorption indicates that CO and O2/O could co-adsorb on Ir subnanometer clusters, however on larger nanoparticle the particle surface is covered by CO only. Additional co-adsorption of CO and O2 was studied by CO and O2 calorimetry at room temperature. CO oxidation results showed that Ir subnanometer clusters are more active than Ir single atoms and Ir nanoparticles at all conditions, and this could be explained by the competitive adsorption of CO and O2 on subnanometer clusters.

High stability Fourier transform infrared (FT-IR) interferometers make it possible to conduct experiments designed to identify subtle sample structure changes resulting from external perturbations. In particular, variable temperature diffuse reflection infrared Fourier transform spectroscopy (VT-DRIFTS) can be used to associate specific structure changes with incremental additions of thermal energy by observing the sample in-situ while heating, eliminating the need for separate measurements that can introduce error. This fast, low cost spectroscopic technique requires a small sample and little preparation, but is complicated by the fact that when the sample is heated, the system is also perturbed rendering the data difficult to interpret due to so-called “thermal artifacts.” Until now little has been done to understand the effect of thermal fluctuations on IR spectra due to instrument response. In this study a thorough characterization of the VT-DRIFTS system led to the identification of the key sources of spectral changes, minimization of thermal artifacts, and development of a data processing method. Experimental studies have shown that the main causes of temperature-induced artifacts are thermal expansion-related alignment changes in the optical transmission system, especially near the sample holder and the interferometer, and modulated and unmodulated thermal emission from the sample itself and instrument components, which affect the detector response at high temperatures. Incorporating thermal barriers and redesigning the sample holder to incorporate quartz has decreased temperature-induced alignment changes. Incorporating cube-corner mirrors at the interferometer and blocking 1/2 the IR beam between the interferometer and the sample solves the modulated thermal emission problem by the method of destructive interference of the noise waives. Unmodulated emission can be accounted for by scaling the data post-collection using computational macros. Thus, the effects of temperature on IR spectra have been characterized, and solutions have been proposed for variable temperature instrument optimization. Capabilities of the VT-DRIFTS methodology are demonstrated in this work.

Currently, the mainstream structure testing methods include physical tests via experiments and numerical simulations. The advantage of physical tests is obvious: the reality can be fully reflected, which means uncertainties and realistic results can be obtained. However, it can be time consuming for the preparation and post processing, as well as it is expensive. Numerical simulations are economic and have simplified operation procedures compared with physical tests, but it may be time consuming to simulate complex structures and cannot reflect the reality such as uncertainties in all circumstances. Then, considering the challenges and the cost of testing the large and complex structures using the physical test, and in order to avoid such disadvantages, several techniques have been proposed to combine the advantages of physical tests and numerical simulations. Such techniques are categorised as the hybrid testing. The basic idea is to place some parts of structures under test with a numerically simulated system while the rest of structures are physically tested. Dynamically structured systems (DSS) are a typical example of hybrid testing; by combining real-time numerical simulation with physical components, it allows dynamic testing of structures with high accuracy and fidelity, while reducing cost. In this thesis, a comprehensive analysis of DSS approaches for generic lumped parameter vibration problems is conducted to find explicit conditions under which DSS is a feasible approach for dynamic testing. At present, the feasibility analysis of generic vibrating structure is missing. The work presented in this thesis is a step towards filling this gap. Analytical conditions for structural properties such as causality, observability and controllability are derived for a generalised vibration system represented as a chain of mass-spring-damper systems. The method is based on a novel recursive representation of transfer functions, which eliminates the need for deriving fully explicit symbolic expressions. This in turn, allows performing the causality analysis without the exact knowledge of the structure parameters, meaning that the procedure can be largely simplified. In addition, it enables the verification of structural properties such as controllability and observability with the reduced knowledge of the structure compared to standard approaches. Such generic conditions complement specific examples available in the literature and provide guidelines for DSS implementation in vibration problems. Another issue tackled in this thesis is the hybrid testing of the marginally stable structures. Previous research neglected this kind of structures or designed controllers that are only valid for specific models, which severely limits their application. This thesis highlights a systematic flaw of the traditional displacements error based feedback control, which makes it difficult to achieve synchronisation and prevent drifts of marginally stable structures. In order to overcome such issues, a novel control architecture is proposed based on the error between velocities instead of the displacements. This method can prevent instability and potential drifts during hybrid testing of marginally stable structures. The benefit also includes the expansion of the applications of DSS to other types of structures.

In recent years, many new buildings have been designed for low-damage seismic performance in order to achieve earthquake resilience, contrary to seismic design codes, which primarily focus on life safety (ASCE/SEI 7-10, 2010). Many researchers have investigated a variety of approaches to increase the resilience of buildings against wind or earthquake loads. Supplemental passive damper systems such as viscous and visco-elastic dampers are considered effective and affordable means of improving seismic resilience. However, these rate-dependent dampers may unintentionally overstress the columns of the building since their force output depends on inter-storey velocity. One way of reducing the aforementioned detrimental effect is to control the peak damper force using a hybrid damper system. This paper evaluates the seismic performance of steel frames equipped with visco-plastic dampers. The visco-plastic damper is realised by the in-series combination of a viscoelastic damper and a friction device. The main goal of exploiting the visco-plastic damper in resilience-based seismic design is the reduction of storey drifts without increasing the base shear force, i.e., a design objective that is difficult to achieve with conventional viscous or viscoelastic dampers. A prototype steel building is designed as a high ductility steel momentresisting frame (MRF). This MRF is then equipped with viscoelastic or viscoplastic dampers to achieve high seismic performance. Parametric designs are carried out by designing visco-plastic dampers with different activation forces for their friction devices. Non-linear dynamic analyses for a set of 20 earthquake ground motions scaled to the design and maximum considered earthquake intensities are carried out in OpenSees. The response results highlight the advantages of the visco-plastic damper. Moreover, they provide the basis to identify the appropriate range of activation force values for the visco-plastic damper's friction device so that drift reduction and control of the peak base shear force can be simultaneously achieved. Also, peak residual drifts that are directly related to the earthquake resilience of the building are compared between the frames with viscoelastic and visco-plastic dampers under DBE and MCE levels. Furthermore, IDA (Incremental Dynamic Analysis) are carried out on the MRFs with viscoelastic and visco-plastic dampers using a set of 20 far-field seismic records to evaluate vulnerability to collapse. The results highlight the effectiveness of the visco-plastic damper in controlling drifts, residual drifts, and the base shear force, while also providing adequate seismic collapse resistance.

Experiencing the body as a coherent, stable, entity involves the dynamic integration of information from several internal (i.e. interoceptive) and external (i.e. exteroceptive) sensory sources, to produce a feeling that the body is mine (sense of body ownership), that I am in control (sense of agency) and I am aware of its movements (motor awareness). However, the exact contribution of these different sensory sources to self-consciousness, as well as the context in which we experience them, is still a matter of debate. This thesis aimed to investigate the neurocognitive mechanisms of body ownership, agency and motor awareness, including interoceptive (via affective touch), proprioceptive, exteroceptive (visuo-spatial) and vestibular contributions to body representation, in both healthy subjects and brain damaged patients. To examine the role of the vestibular and interoceptive systems in body ownership, a series of studies in healthy subjects was devised, using multisensory illusions (i.e. the rubber hand illusion; RHI), that involve the integration of interoceptive and exteroceptive sensory sources, and using electrical stimulation of the vestibular system (i.e. Galvanic Vestibular Stimulation; GVS). To investigate ownership, agency and motor awareness in neuropsychological patients with disorders of ownership and/or unawareness of motor deficits, behavioural manipulation of body ownership (via a rubber hand) and visual perspective (via a mirror) were tested. Finally, to explore underlying mechanisms of awareness of one’s own performance (i.e. meta-cognition), two studies were carried out in healthy subjects using behavioural manipulations of spatial reference frames (either centred on the subject, i.e. egocentric, or world-centred, i.e. allocentric). The results of these studies indicate that the vestibular system balances vision and proprioception according to contextual relevance: when there is no tactile stimulation, visual cues are stronger than proprioceptive ones (i.e. proprioceptive drifts are greater); when touch is delivered synchronously, this effect is enhanced (even more when touch is affective rather than neutral). However, when touch is only felt but not seen, the vestibular system downregulates vision in favour of proprioception (i.e. proprioceptive drifts are smaller), whilst the opposite happens when touch is only vicariously perceived via vision. Nevertheless, when the rubber hand is positioned in a non-biomechanically possible fashion, there appears to be no difference in proprioceptive drifts in comparison with anatomically plausible positions, suggesting that such rebalancing may be more related to basic multisensory integration processes underlying body representation. In patients with disorders of the self, visual cues seem to dominate over proprioceptive ones, leading to strong feelings of ownership of a rubber hand following mere exposure to it; however, the same is not true for agency, which seems to be more susceptible to changes in the environment (i.e. presence or absence of a visual feedback following attempted movement). Moreover, manipulating visual perspective using a mirror (from 1st to 3rd) seem to lead to a temporary remission of dis-ownership but not motor unawareness, suggesting that awareness may not be influenced by online changes in visual perspectives. Finally, when judging their own performance in a visuo-proprioceptive task from an egocentric rather than an allocentric perspective, healthy subjects appear less objective prospectively rather than during the task (i.e. their belief updating is biased when judging their ability to complete a task egocentrically). In sum, the work described above adds to the evidence that the sense of self derives from a complex integration of several sensory modalities, flexibly adjusting to the environment. Following brain damage, such flexibility may be impaired, even though it can be influenced by spatial perspective. Similarly, the point of reference from which we perceive stimuli affects the way we judge our own perceptual choices. Hence, the way we represent our bodily self is a dynamic process, constantly updated by exteroceptive and interoceptive incoming stimuli, regulated by the vestibular system. These findings could provide new avenues in rehabilitating disorders of the self (such as unawareness and dis-ownership).

Heterogeneous catalyst materials, in particular supported metal nanoparticles, are hugely important in industrial processes such as emissions control technology. In order to design new catalyst materials with improved catalytic performance, the catalytic properties and mechanistic pathways must be well understood. The work presented in this thesis makes advances to operando spectroscopy methods for the characterisation of industrial heterogeneous catalyst materials operating in realistic reaction conditions. A multi-technique approach, employing XAFS and DRIFTS simultaneously, has been used to probe the electronic and structural properties of metal nanoparticles as well as the molecular vibrations of reactants and intermediates at the catalyst surface. In this work, the preparation of supported metal nanoparticle catalysts have been investigated using time-resolved XAFS and DRIFTS spectroscopy approach. Structure-activity relationships of the supported Pd nanoparticle catalysts have been identified during their operation for an important reaction used in diesel after-treatment technology; the selective catalytic oxidation of NH3. The selectivity of the reaction towards the different reaction products (N2, N2O and NO), within the relevant temperature window for industrial application, has been linked to the different structural phases of Pd, including a previously unidentified PdNx species. The major challenges for operando measurements, concerning improved time resolution and spatial resolution, have been addressed with the development of a new reactor for combined XAFS and DRIFTS of a catalyst bed operating in plug-flow reaction conditions. The advantage of this reactor design for operando characterisation has been demonstrated in the investigation of a supported Pd nanoparticle catalyst (Pd/γ-Al2O3) during oscillating CO oxidation. This reactor improves on previous designs by allowing spectroscopic measurements to be performed with spatial resolution along the axial length of a catalyst bed. In this way, there is scope for elucidating the structure-function relationships of many other heterogeneous catalysts.

The advances in computing software, hardware, connected devices and wireless communication infrastructure in recent years have led to the desire to work with streaming data sources. Yet the number of techniques, approaches and algorithms which can work with data from a streaming source is still very limited, compared with batched data. Although data mining techniques have been a well-studied topic of knowledge discovery for decades, many unique properties as well as challenges in learning from a data stream have not been considered properly due to the actual presence of and the real needs to mine information from streaming data sources. This thesis aims to contribute to the knowledge by developing a rule-based algorithm to specifically learn classification rules from data streams, with the learned rules are expressive so that a human user can easily interpret the concept and rationale behind the predictions of the created model. There are two main structures to represent a classification model; the 'tree-based' structure and the 'rule-based' structure. Even though both forms of representation are popular and well-known in traditional data mining, they are different when it comes to interpretability and quality of models in certain circumstances. The first part of this thesis analyses background work and relevant topics in learning classification rules from data streams. This study provides information about the essential requirements to produce high quality classification rules from data streams and how many systems, algorithms and techniques related to learn the classification of a static dataset are not applicable in a streaming environment. The second part of the thesis investigates at a new technique to improve the efficiency and accuracy in learning heuristics from numeric features from a streaming data source. The computational cost is one of the important factors to be considered for an effective and practical learning algorithm/system because of the needs to learn from continuous arrivals of data examples sequentially and discard the seen data examples. If the computing cost is too expensive, then one may not be able to keep pace with the arrival of high velocity and possibly unbound data streams. The proposed technique was first discussed in the context of the use of Gaussian distribution as heuristics for building rule terms on numeric features. Secondly, empirical evaluation shows the successful integration of the proposed technique into an existing rule-based algorithm for the data stream, eRules. Continuing on the topic of a rule-based algorithm for classification data streams, the use of Hoeffding's Inequality addresses another problem in learning from a data stream, namely how much data should be seen from a data stream before starting learning and how to keep the model updated over time. By incorporating the theory from Hoeffding's Inequality, this study presents the Hoeffding Rules algorithm, which can induce modular rules directly from a streaming data source with dynamic window sizes throughout the learning period to ensure the efficiency and robustness towards the concept drifts. Concept drift is another unique challenge in mining data streams which the underlying concept of the data can change either gradually or abruptly over time and the learner should adapt to these changes as quickly as possible. This research focuses on the development of a rule-based algorithm, Hoeffding Rules, for data stream which considers streaming environments as primary data sources and addresses several unique challenges in learning rules from data streams such as concept drifts and computational efficiency. This knowledge facilitates the need and the importance of an interpretable machine learning model; applying new studies to improve the ability to mine useful insights from potentially high velocity, high volume and unbounded data streams. More broadly, this research complements the study in learning classification rules from data streams to address some of the unique challenges in data streams compared with conventional batch data, with the knowledge necessary to systematically and effectively learn expressive and modular classification rules from data streams.

Through in-depth analyses of nine of Mendelssohn’s Lieder ohne Worte, this paper argues that all nine pieces follow a single underlying event-arc typified by a cyclical drift into and out of the mediant key area. When considered in the specific context of Mendelssohn’s philosophical approach to song writing, the pianistic and cultural context of the pieces, and the connotations of the Lied genre along with strategic musical features common to all nine pieces, such as consistent accompaniment texture and relatively unstable opening melodic motives, this drift can be characterized as a musical embodiment of the internal experience of daydreaming. Though the nature and content of the “daydream” arc in each piece is different, the sense of drifting away and effortless return characteristic of this event-arc is always the same.

Model ceria catalysts were evaluated for NOx storage and desorption performance under lean conditions. Three different storage temperatures (80 °C, 120 °C, and 160 °C) were utilized to evaluate NOx storage. Higher temperatures resulted in higher NOx storage. It was observed that storage of platinum promoted ceria resulted in higher NOx storage compared to promotion with palladium. NOx desorption behavior of platinum promoted ceria indicated that the majority of NOx is released at high temperatures (> 350 °C), comparatively palladium promotion released more of the stored NOx at lower temperatures. Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) indicated that platinum promotion results in NOx storage as thermally stabile nitrates, while palladium promotion results in NOx storage as thermally labile nitrites. Doping ceria with trivalent rare earth oxides has been shown to improve NOx storage by generating lattice oxygen vacancies. Ceria doped with Pr, Y, La, Sm, and Nd at two different concentrations (5 and 20 mol%) and promoted with Pt were evaluated. Doping ceria with 5% Sm, Nd, and Pr improved the amount of NOx stored while the addition of Sm and La did not improve storage. Upon increasing dopant concentration, NOx storage decreased in all cases but Pr. However, increasing Pr concentration was found to increase NOx storage as well as low temperature NOx release. Ceria doped with Pr promoted with Pd increased the amount of NOx released at lower temperatures compared to Pt promotion, although palladium promotion resulted in lower storage. Similar DRIFTS spectra were obtained with Ce-Pr when promoted with Pt or Pd compared to model catalysts. Platinum promotion results in the storage of NOx at nitrates, which require high temperatures for removal. Comparatively, Pd promotion results in NOx stored at nitrites requiring lower temperatures for removal. Ceria doped with Pr proved to be promising, although not thermally stable when exposed to high temperatures as may be seen during a DPF clean up. Therefor, stabilizing Ce-Pr catalysts with Zr were evaluated. It was found that stabilizing Ce-Pr with Zr was not found to be beneficial to the catalyst performance.

This thesis presents the first high-resolution palaeoceanographic study of environmental changes in the Amundsen Sea sector of the West Antarctic continental margin during the Late Quaternary. This part of the West Antarctic Ice Sheet (WAIS) is currently experiencing rapid mass loss and longer-term records can provide important context for these changes. Four piston cores, covering the last c. 375 kyrs, have been studied from two of the five large sediment mounds which stand on the continental rise of the eastern Amundsen Sea. Four of the mounds have been previously been identified in the literature as sediment drifts. The cores were analysed for sedimentology (grain size, physical properties, spectrophotometry), mineralogy (clay minerals, sand fraction composition) and geochemistry (XRF, biogenic silica content, TOC, CaCO3). These data were used to infer the supply of terrigenous material from the West Antarctic Ice Sheet, the amount of biological productivity and the nature of the bottom current. Age constraints for the ≤375 kyr records are derived from relative palaeomagnetic intensity, diatom biostratigraphy, AMS 14C dates, tephrochronology and lithostratigraphy. Analysis of the sediments together with new geophysical and bathymetric data suggests the mounds are mixed contourite-turbidite drifts. Turbidity currents were initiated at the margins of, and between, the mouths of Pine Island Trough East and West and Abbot Trough. The turbidity currents eroded channels in the slope, some of which connect to the deeply incised, maximum 20 km wide and 400 km long channels separating the drifts. The fine-grained fraction of the turbidity currents was pirated and deposited on the drift crests by the weak, eastwards-flowing bottom current, which may be Antarctic Bottom Water or Lower Circumpolar Deep Water. The coarse-grained component of the turbidity currents was largely constrained to the channels, with occasional spill-over depositing sand and sandy muds on the drift flanks. The drifts are long (250-433 km), narrow (38-130 km), stand up to 900 m above the sea floor and are asymmetric, with a gently-sloping western flank and steeper eastern flank resulting from sediment interaction with the bottom current. The sediments exhibit strong cyclicity corresponding to glacial-interglacial cycles. During glacial periods, deposition was mostly of grey, terrigenous, typically laminated contourites with dispersed ice rafted debris and locally-sourced kaolinite and illite. Sedimentation rates ranging from 0.1 to 17.2 cm/kyr reflect WAIS advance and retreat across the shelf. Bottom currents captured the fine grained fraction of turbidity currents and deposited laminated sediments, similar to those reported in the drifts west of the Antarctic Peninsula, attributed to a steady bottom current and the absence of bioturbation under perennial sea-ice. Manganese contents suggest suboxic conditions during glacial periods. A laminated sand and sandy mud turbidite deposit is present in a drift flank core. Olive-brown, bioturbated, diatom-bearing and often calcareous-foraminifera-bearing, mixed contourite and hemi-pelagic muds were deposited in interglacial periods. Sedimentation rates range from 0.2 to ≥3.7 cm/kyr reflecting changes in productivity that were mostly controlled by sea-ice coverage. The smectite content of surface samples from the drifts are larger than any other known sample from the Amundsen Sea shelf or rise and suggest that the bottom current also deposits far-travelled clay. There are no major depositional anomalies or thick IRD layers in the drift cores that might indicate collapse of the WAIS. However, millennial-scale cyclical variations in the provenance of terrigenous material in PS58/255-2 during mid-late MIS 6 may reflect changes in ice dynamics.

Data streams are unbounded, sequential data instances that are generated with high Velocity. Data streams arrive online (i.e., instance by instance) and there is no control over the order in which data instances arrive either within a data stream or across data streams. Classifying sequential data instances is a challenging problem in machine learning with applications in network intrusion detection, financial markets and sensor networks. The automatic labelling of unseen instances from the stream in real-time is the main challenge that data stream classification faces. For this, the classifier needs to adapt to concept drifts and can only have a single-pass through the data with a limited amount of memory if the stream is generating data instances at a high Velocity. Nowadays the focus of Data Stream Mining (DSM) lies in the development of data mining algorithms rather than on pre-processing techniques. To the best of the author knowledge, at present, there are no developments for truly real-time feature selection in a streaming setting. This research work presents a real-time pre-processing technique, in particular, feature tracking in combination with concept drift detection. The feature tracking is designed to improve DSM classification algorithms by enabling real-time feature selection. The pre-processing technique is based on tracking adaptive statistical summaries of the data and class label distributions, known as Micro-Clusters. Thus the three objectives of this research were to develop a real-time pre-processing technique that can (1) detect a concept drift, (2) identify features that were involved in concept drift and thus potentially change their relevance and (3) build a real-time feature selection method based on the developments mentioned above. The evaluation of the developed technique is based on artificial data streams with known ground truth and real datasets with and without artificially induced concept drift (i.e., controlled and uncontrolled real datasets). It was observed that the developed method for concept drift detection did detect induced concept drifts very well compared with alternative concept drift detection methods. Overall the research represents a first attempt to resolve real-time feature selection for DSM tasks. It has been shown that the technique can indeed identify concept drift, track features, and identify features that may have changed their relevance for the DSM task in real-time. It has also been shown that the developed method for real-time feature selection can improve the accuracy of data stream classification tasks.

Solid oxide electrocatalytic cell (SOEC) is a promising technology for facilitating reactions involving the removal or addition of oxide ions. By designing the appropriate electrode catalysts, reactions such as CO2 electrolysis, H2O electrolysis, oxidative dehydrogenation (ODH) of light alkanes (C2H6, C3H8), and oxidative coupling of methane can be efficiently carried out in an SOEC. Perovskite materials based on Fe, such as La(Sr)FeO3, possess a range of beneficial properties for use in electrocatalysis. They are compatible with electrolyte materials, have high electrical conductivity, and are stable in both oxidizing and reducing atmospheres. Additionally, the formation of exsolved B-site metal nanoparticles on the surface of the perovskite material can significantly improve its electrochemical activity when exposed to a reducing atmosphere.In chapter 1, La0.7Sr0.2Ni0.2Fe0.8O3 (LSNF), having thermochemical stability, superior ionic and electronic conductivity, and structural flexibility, was investigated as a cathode in SOECs. Exsolution of nanoparticles by reduction of LSNF at elevated temperatures can modulate the characteristics of adsorption, electron transfer, and oxidation states of catalytically active atoms, consequently improving the electrocatalytic activity. The exsolution of NiFe and La2NiO4 nanoparticles to the surface of LSNF under reducing atmosphere (5% H2/N2) was verified at various temperatures (500–800 °C) by IFFT from ETEM, TPR and in situ XRD. The exsolved nanoparticles obtained uniform size distribution (4.2–9.2 nm) and dispersion (1.31 to 0.61 × 104 particle per μm2) depending on the reduction temperature (700–800 °C) and time (0–10 h). The reoxidation of the reduced LSNF (Red-LSNF) was verified by the XRD patterns, indicative of its redox ability, which allows for redistribution of the nanoparticles between the surface and the bulk. TPD-DRIFTS analysis demonstrated that Red-LSNF had superior H2O and CO2 adsorption behavior as compared to unreduced LSNF, which we attributed to the abundance of oxygen vacancy sites and the exsolved NiFe and La2NiO4 nanoparticles. After the reduction of LSNF, the decreases in the oxidation states of the catalytically active ions, Fe and Ni, were characterized on the surface by XPS as well as in the bulk by XANES. The electrochemical performance of the Red-LSNF cell was superior to that of the LSNF cell for electrolysis of H2O, CO2, and H2O/CO2.In chapter 2, oxidative coupling of methane (OCM), which is a fundamentally challenging reaction due to low selectivity of C2+ hydrocarbons, was investigated using a solid oxide electrocatalytic cell. This study presents in-situ reduction of lanthanum strontium cobalt ferrite (LSCF) perovskite as an effective method for modifying its surface properties and enhancing its electrocatalytic reactivity for OCM. The evolution of hetero-phases during the reduction of LSCF resulted in CoFe nanoparticles being formed at the surface. The in-situ reduced LSCF cell for OCM could be operated in either an ion pump or a fuel cell mode. High selectivity of 63% and 10.2% were reported for C2+ hydrocarbons and C3H6, respectively. DFT calculations on LSCF and CoFe revealed that the high selectivity of C2+ hydrocarbons on the LSCF primarily stems from the presence of CoFe nanoparticles. In addition, elemental analysis of the surface and bulk LSCF by XPS and XANES verified the reduced oxidation state of Co and Fe ions. In-situ TPD-DRIFTS spectra using CO2 as a probe molecule indicated enhanced basicity on the reduced LSCF. Temperature-programmed experiments and in-situ DRIFTS conducted under CH4 flow proved that complete oxidation of CH4 can be effectively inhibited by reducing LSCF, and control of oxygen supply is an important parameter for selective conversion of CH4 to higher order hydrocarbon.In chapter 3, La0.7Sr0.2Ni0.2Fe0.8O3 (LSNF) perovskite was used an effective anode for OCM with co-feeding of water. Its surface characteristics and electrocatalytic activity can be improved by reduction and the resultant exsolution of bimetallic NiFe nanoparticles from its bulk. X-ray diffraction (XRD) and environmental transmission electron microscopy proved that the evolution of hetero-phases under reducing environment resulted in bimetallic NiFe nanoparticles being formed on the surface. A 36% improvement in C2+ hydrocarbon production rate was achieved due to the reduction of LSNF and the formation of exsolved NiFe nanoparticles. Co-feeding of H2O enhanced selective conversion of CH4 resulting in the production rate of C2+ hydrocarbons being increased by 56%. Analysis of impedance spectra and in-situ DRIFTS under a CH4 + H2O atmosphere provided an understanding for the enhancement on the electrocatalytic OCM.Chapter 4 investigates the exsolution of metal nanoparticles from perovskite oxide as a method to enhance the electrochemical performance in solid oxide electrocatalytic cells. The process can be electrochemically triggered by applying cathodic polarization to the perovskite oxide electrode for a short period of time. The electrochemical reduction of the perovskite oxide electrode and the resultant exsolution of B-site metal nanoparticles are investigated by scanning the cell voltage to provide an insight into the electrochemical behavior during electrolysis. A user-designed X-ray absorption spectroscopy operando cell is used to identify the edge energy change of B-site atoms during electrolysis. The electrochemical switching point is identified as a point where the incremental area-specific resistance changes to a decrease, providing valuable insight into the electrochemical exsolution process of metal nanoparticles in perovskite oxide electrodes. Chapter 5 explored oxidative dehydrogenation (ODH) of propane which offers an attractive and feasible method for increasing propylene production to meet the growing demand. A dual phase catalyst composed of proton conducting SrCe0.95Yb0.05O3 (SCY) and mixed oxygen ionic and electronic conducting (La0.8Sr0.2)0.95MnO3 (LSM) has been investigated for propane ODH. TPD-DRIFTS was used to study H2O adsorption and the presence of basic lattice oxygen on the surface of SCY. The addition of 40 wt% SCY to LSM resulted in significant improvements in propane conversion and olefin selectivity. These enhancements were likely due to the highly basic nature of the SCY surface by helping rapid desorption of propylene resulting in high propylene selectivity. In addition, proton conductive SCY facilitated the proton abstraction from propane and its transport to the interface between SCY and LSM where an oxidation reaction occurs. It was demonstrated by TPrxn experiments using C3H8 and C3H6 that a moderate oxygen ionic conductivity and a basic surface of the catalyst are needed for the selective conversion of propane to olefins.Chapter 6 proposes potential future research on enhancing the structural stability of perovskite oxide through the incorporation of oxyphilic atoms like Mo or Cr. This is in response to the exsolution of B-site atoms, which creates oxygen vacancies in the lattice and potentially undermines stability. This stability issue caused by exsolution of b-site atoms could be addressed by oxyphilic atom inversion. In addition to this, it is suggested that double perovskite oxides can serve as superior electrode materials due to their enhanced stability in both reducing and oxidizing environments compared to traditional perovskite oxides. This provides a promising alternative to ferrite-based perovskites.

Several authors have reported inner magnetosphere observations of electron and proton distributions confined to narrow energy bands in the range 1 - 25 keV (e.g Smith and Hoffman (1974)). These structures have been described as “nose structures”, with reference to their appearance in energy-time spectrograms, and are also known as “bands” if they occur for extended periods of time. These structures have been explained in terms of the competing magnetic and electric drifts (ions only; e.g. Buzulukova et al. (2003)), possible charge exchange losses in slow drift paths (ions only; e.g. Kistler et al. (1989)) and trapped injected populations (electrons + ions; e.g. Liemohn et al. (1998)). We analyse observations of electron and ion “multi-band structures” (2 or more bands at a given instance) seen in Double-Star TC1 PEACE and HIA data, mainly recorded in the equatorial plane of the inner magnetosphere (L < 15). The first two studies of this thesis present results of statistical surveys analysing distributions of electron and ion multi-bands and the conditions required for their formation. We find that these features are likely to be during times of quiet geomagnetic activity following a substorm. Band populations originate in the tail plasma sheet and lower energy bands (W ≈ 100’s eV - few keV) are observed as cold dense plasma sheet populations drift towards the inner magnetosphere. The three latter studies of this thesis focuses on using a simple particle drift model to try and illustrate whether drifts alone could create electron and ion multi-bands structures. We test drift in quiet time steady state conditions, drift after a substorm and drift in a steady state magnetosphere + diurnal electric field and show that multi-bands can be created in a number of ways for both electrons and ions.

Improving seismic resilience of buildings is one of the current challenges in structural engineering. In the context of steel structures, design of conventional systems in accordance to current codes aims at preventing collapse and ensuring life safety under the design earthquake. However, in major seismic events, these systems have experienced extensive damage in the main structural membersand large residual drifts, causing downtime and significant socio-economic losses. This thesis presents the development and validation of an innovative dual steel frame that reduces structural damage and residual drifts for enhanced seismic performance. The proposed system consists of a moment-resisting frame with concentric braces equipped with seismic dampers. These are stainless steel pins with high post-yield stiffness, placed in series with the bracing members. Replaceable elements are inserted in the beams to absorb plastic deformations that would concentrate in the beam-column connections. The seismic performance of the proposed dual frame is evaluated using experimentally-validated finite element models of a prototype steel building. The numerical results show that, under the design and maximum earthquakes, residual storey drifts are minimised due to the high post-yield stiffness of the seismic dampers and the elastic deformation capacity of the moment frame. Structural damage is concentrated in the replaceable seismic devices, indicating the potential for a quick recovery after a strong earthquake. The collapse potential of the proposed frame is also investigated. The fracture capacity of the seismic dampers is experimentally evaluated using two full-scale geometries in a configuration reproducing the damper-brace connection. Criteria for predicting ductile fracture under ultra-low cycle fatigue are calibrated using coupon specimens and complementary finite element analyses, and validated performing explicit simulations of the full-scale tests. The collapse of the dual frame is studied by means of incremental dynamic analyses explicitly simulating the ductile fracture of the seismic dampers. The results show that the dual frame has a superior seismic resistance against collapse as a result of the large energy dissipation and fracture capacities of the seismic dampers.

Nonstructural damage has been found to critically influence economic losses and building downtime following earthquakes. Attaining a target level of seismic performance mandates the harmonisation of structural and nonstructural performance. Retrofitting buildings with fluid viscous dampers (FVDs) can improve interstorey drifts and floor accelerations, parameters which characterise seismic demands. The distribution of dampers within a building and the amount of supplemental damping are critical decisions. Many placement methods have been proposed, however no conclusive optimal method has been identified. The objectives of the thesis included benchmarking the performance of code-compliant buildings, investigating the optimal amount of damping, and comparing major damper placement methods. The seismic performance of Eurocode-compliant concentric braced frame buildings was benchmarked. Structures were modelled in OpenSees and the FEMA P-58 procedure was used to assess seismic performance in repair costs, a comprehensive measure of total-building performance. Buildings may be demolished following an ultimate limit state earthquake due to high repair costs. Storeys satisfying the Eurocode drift limit for nonstructural protection nevertheless experienced drift- and acceleration-sensitive damage. Most damage was attributed to nonstructural systems. Acceleration-sensitive damage is of comparable or greater consequence than drift-sensitive damage. These findings should be more appropriately reflected in structural design procedures. The optimal amount of damping to minimise repair costs was identified as 30-40%, larger than previously suggested levels based on structural parameters. Six damper placement methods were evaluated using linear FVDs. No method produced optimal results for both drifts and accelerations. Iterative methods that purport to optimise performance did not achieve that objective: local rather than global parameters are considered, and optimising for a single parameter may worsen another that impacts damage. The storey shear strain energy method and uniform damping produced repair costs more favourable than, or equal to, the other placement methods. Damper placement optimisation may be successful for high-rise or irregular structures.

This thesis presents work seeking to elucidate the active site and the mechanism of ozone generation at nickel and antimony-doped tin oxide (NATO) electrodes. To this end, tin oxide (TO, SnO2), antimony-doped tin oxide (ATO, Sb-SnO2) and nickel-antimony doped tin oxide (NATO, Ni/Sb-SnO2) nanopowders were prepared via a hydrothermal (HT) method and either left uncalcined or calcined at 300, 400 and 700 oC. The nanopowders were characterised using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), measurement of surface area by the Brunauer Emmett Teller (BET) technique, thermogravimetric analysis (TGA), diffuse reflectance Fourier Transform Infra-Red spectroscopy (DRIFTS) and X-ray Photoelectron Spectroscopy (XPS). The electrochemical ozone activity and selectivity of the powders were also determined in 0.5M H2SO4 and compared to those of ceramic anodes prepared via conventional methods. All the nanopowders showed a single cassiterite phase with crystallite sizes that varied with composition and calcination temperature. The BET surface areas of the nanopowders decreased with increasing calcination temperature and also on doping with Sb and Ni. The BET surface areas in general were smaller than those calculated from XRD, suggesting the agglomeration of crystallites to form larger grains. Addition of Sb to undoped SnO2 resulted in a significant increase in the number of crystallites per grain. Co-doping with Ni initially caused a large reduction in the number of crystallite per grain, but not back to the undoped value, with additional Ni having little or no effect. The ozone activities and selectivity of the nanopowders were studied by UV-Vis spectroscopy in 0.5M H2SO4 by deposition onto Ti foil substrates and using a UV-Vis cuvette as the electrochemical cell. The data so obtained were compared to results using a ceramic Ni/Sb-SnO2 anode prepared via the conventional method. All the Ni/Sb-SnO2 nanopowders calcined at 400 oC were inactive with respect to ozone, whilst the Ni/Sb-SnO2 nanopowders calcined at 700 oC were all active, showing comparable current densities and ozone current efficiencies to those observed using the ceramic anodes. This was the first work to show ozone generated with high selectivity and activity at Ni/Sb-SnO2 nanopowders. Durability studies on a ceramic anode showed no change in ozone activity or selectivity over a 10 day period, supporting the results of earlier such studies in Newcastle and strongly suggesting that the Ni species responsible for ozone evolution at Ni/Sb-SnO2 is not located at the surface. A key aspect of the research programme was the study of undoped SnO2 calcined at 400 oC and 700 oC using BET, XRD, TGA-MS and in-situ variable temperature DRIFTS. BET showed the relatively high surface area and nanometer scale of the SnO2 particles, whilst XRD confirmed the nano dimension of the crystallites and showed only the cassiterite phase. TGA analysis indicated four temperature regions over which mass loss was observed. These and the in-situ DRIFTS studies revealed the existence of various forms of water associated with specific crystal facets of the SnO2, as well as existence of isolated O-H groups and adsorbed oxygen species. For the (100) facets, hydrogen bonding does not occur, and water absorption is less strong than for the (111) and (110) facets where hydrogen bonding does occur. On the (100) facets, the hydrogen atoms of the OH groups are located in cavities in the plane of the O atoms, and hence are unavailable for hydrogen bonding. In contrast, the H atoms on the (111) and (110) facets are available. The samples calcined at 700 oC showed significantly less adsorbed water than those calcined at 400 oC, and this could be attributed to lower coverage by OH on the former. The reversible uptake of oxygen was observed in the TGA studies, and this seeded the development of the final model. Electronic absorptions were also observed and the data rationalised in terms of the existence of both free electron absorptions, and absorptions from oxygen vacancy states. XPS of the Sb-containing nanopowders (i.e. Sb-SnO2 and Ni/Sb-SnO2) showed Sn in the +4 oxidation state, whilst Sb was present as both Sb(III) and Sb(V), and Ni as Ni(II) and Ni(III). Combining these studies with TGA-MS, it was shown that Sb(V) ions substitute for Sn(IV) in the lattice, with a preference for centrosymmetric coordination sites whilst the Sb(III) ions occur at the grain boundaries or surface. The Sb(V) ions confer electronic conductivity on the SnO2 whilst both Sb(III) and Ni are essential for O3 generation. The Ni occupies Sn(IV) sites in the subsurface region at concentrations below the detection limit of XPS. A model was postulated on the basis of the data, as well as a mechanism for ozone generation. The remediation of the Reactive Blue dye (RB50) in 0.5M H2SO4 was studies using both powder and ceramic anodes. Decolourization of RB50 solution was achieved within minutes of electrolysis, with COD and TOC removal of more than 80%. In addition to identifying a possible mechanism for ozone formation, the work reported in this thesis resulted in the production of active nanopowders which will allow the fabrication of high surface-area anodes with the potential to exceed the space-time yield of β-PbO2 anodes, permitting the application the Ni/Sb-SnO2 anodes in the treatment of real waters. A key aspect of the research programme was the study of undoped SnO2 calcined at 400 oC and 700 oC using BET, XRD, TGA-MS and in-situ variable temperature DRIFTS. BET showed the relatively high surface area and nanometer scale of the SnO2 particles, whilst XRD confirmed the nano dimension of the crystallites and showed only the cassiterite phase. TGA analysis indicated four temperature regions over which mass loss was observed. These and the in-situ DRIFTS studies revealed the existence of various forms of water associated with specific crystal facets of the SnO2, as well as existence of isolated O-H groups and adsorbed oxygen species. For the (100) facets, hydrogen bonding does not occur, and water absorption is less strong than for the (111) and (110) facets where hydrogen bonding does occur. On the (100) facets, the hydrogen atoms of the OH groups are located in cavities in the plane of the O atoms, and hence are unavailable for hydrogen bonding. In contrast, the H atoms on the (111) and (110) facets are available. The samples calcined at 700 oC showed significantly less adsorbed water than those calcined at 400 oC, and this could be attributed to lower coverage by OH on the former. The reversible uptake of oxygen was observed in the TGA studies, and this seeded the development of the final model. Electronic absorptions were also observed and the data rationalised in terms of the existence of both free electron absorptions, and absorptions from oxygen vacancy states. XPS of the Sb-containing nanopowders (i.e. Sb-SnO2 and Ni/Sb-SnO2) showed Sn in the +4 oxidation state, whilst Sb was present as both Sb(III) and Sb(V), and Ni as Ni(II) and Ni(III). Combining these studies with TGA-MS, it was shown that Sb(V) ions substitute for Sn(IV) in the lattice, with a preference for centrosymmetric coordination sites whilst the Sb(III) ions occur at the grain boundaries or surface. The Sb(V) ions confer electronic conductivity on the SnO2 whilst both Sb(III) and Ni are essential for O3 generation. The Ni occupies Sn(IV) sites in the subsurface region at concentrations below the detection limit of XPS. A model was postulated on the basis of the data, as well as a mechanism for ozone generation. The remediation of the Reactive Blue dye (RB50) in 0.5M H2SO4 was studies using both powder and ceramic anodes. Decolourization of RB50 solution was achieved within minutes of electrolysis, with COD and TOC removal of more than 80%. In addition to identifying a possible mechanism for ozone formation, the work reported in this thesis resulted in the production of active nanopowders which will allow the fabrication of high surface-area anodes with the potential to exceed the space-time yield of β-PbO2 anodes, permitting the application the Ni/Sb-SnO2 anodes in the treatment of real waters.

Conventional seismic-resistant structural systems are currently designed to develop a global sway plastic mechanism under strong earthquakes, which is achieved by allowing the development of controlled inelastic deformations in specific locations of main structural members such as beams, bases of columns and braces. Inelastic deformations in structural members result in damage and residual drifts, and therefore, in economic losses such as repair costs and downtime. Moreover, earthquake reconnaissance reports reveal large economic losses related to non-structural damage, e.g. failure of walls due to large storey drifts or failure of acceleration-sensitive equipment due to large peak floor accelerations. These losses highlight the need for resilient structures with the potential to remain intact after frequently occurred earthquakes and return to service within an acceptable short, if not immediate, time after strong rare earthquakes. Moreover, resilient structures should provide a very low probability of collapse (i.e. increased life safety) under very rare maximum considered earthquake. Steel self-centering moment-resisting frames using post-tensioned beam-column connections are a promising class of resilient structures. They exhibit softening force-drift behaviour and eliminate inelastic deformations and residual drifts as the result of gap openings developed in beam-column interfaces and elastic post tensioned bars which clamp beams to columns and provide self-centering capability. Also, post tensioned connections use energy dissipation devices, which are activated when gaps open and can be easily replaced if damaged. Steel frames equipped with passive dampers are another class of resilient structures. Dampers provide supplemental damping to control drifts, and thus, provide an effective means to achieve economical designs with high performance. The main goal of this PhD is to develop a seismic design and assessment procedure for steel self-centering moment-resisting frames (SC-MRFs) with viscous dampers within the framework of Eurocodes 3 and 8. To achieve this goal, nonlinear models of post-tensioned connections, able to capture the strength and stiffness deterioration due to local buckling, are developed. These models enable the assessment of the seismic behaviour of SC-MRFs with viscous dampers up to collapse with the aid of nonlinear dynamic analysis. A seismic design method, which incorporates a robust way to estimate rotation demands in post-tensioned connections, is then formulated. Different SC-MRFs with viscous dampers are designed using the proposed design procedure to study different design scenarios. The accuracy of the design procedure is evaluated through nonlinear dynamic analysis. In addition, the superior collapse resistance of SC-MRFs with viscous dampers is validated through incremental dynamic analysis. The thesis concludes with the implementation of an advanced probabilistic framework for direct economic seismic loss estimation and its application to confirm the potential of SC-MRFs with viscous dampers to significantly reduce economic seismic losses.

Conventional seismic-resistant moment-resisting frames (MRFs) designed to Eurocode 8 develop inelastic deformations in beams and column bases that result in residual drifts and structural damage. Self-centering MRFs (SC-MRFs) with post-tensioned beam-column connections have been proposed to address these problems. However, SC-MRFs cannot avoid plastic hinges in their column bases, resulting in difficult-to-repair damage and first-storey residual drifts. Therefore, a damage-free self-centering column base connection (referred to as column base) is needed to further enhance the earthquake resilience of SC-MRFs. A novel column base is proposed in this study. The column base uses post-tensioning technology to achieve self-centering behaviour and replaceable web-hourglass shape steel pins (WHPs) to dissipate seismic energy, while the rest of its components remain damage-free. The column base requires no field welding, it is easy to construct, deconstruct and rehabilitate, and thus facilitates sustainable (green) construction. The study develops an analytical model to predict the column base hysteretic behaviour and identify its limit states. A performance-based design procedure is developed for the column base which ensures self-centering and damage-free behaviour and designs its main components. A high-fidelity nonlinear finite element method (FEM) model for the column base is developed in software Abaqus. The Abaqus model verifies the accuracy of the analytical model and identifies failure modes that cannot be predicted analytically. A FEM model for the column base is also developed in software OpenSees. The model validates the accuracy of the previous models and facilitates the seismic assessment of the novel column base. Design recommendations for practical application to real buildings are also provided. The conventional MRF of a prototype steel building is redesigned as: (a) SC-MRF with conventional column bases; and (b) as SC-MRF with the novel column bases. The two design cases are modelled in OpenSees and nonlinear analyses are performed to compare their seismic response. The results of the analyses demonstrate the resilient performance of the novel column base under strong earthquakes, achieving self-centering and damage-free behaviour. The beneficial effect of the novel column base on the seismic response of SC-MRFs is demonstrated by reducing their peak and residual drifts in the upper storeys, eliminating the latter in the first storey, and pushing their critical structural limit states to higher seismic intensities. Recommendations for future research are also provided.

Heterogeneous catalysis plays a significant role in the chemical industry and the global economy. Most heterogeneous catalysts in the chemical industry and laboratory consist of supported metal nanoparticles, clusters and isolated (single) atoms. Understanding structure sensitivity and identifying the active site or sites are crucially essential for designing efficient catalysts. To determine the active sites of a catalyst for a particular chemical reaction, in-situ/operando spectroscopy, such as diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and X-ray absorption fine structure (XAFS) spectroscopy, is usually implemented as characterization tools. However, understanding the limitation of the characterization tools is crucial to eliminate misleading conclusions. Therefore, the main object of this work is not only to characterize the catalyst before and after the reaction but to investigate the reliability of the characterization tools as well as the stability of the metal clusters and single atoms during CO oxidation. There are four main findings that will be present in this work. First, a high-flux X-ray beam can induce structural change that leads to a reduction of the metal and agglomeration of metal clusters. This finding is very important since X-ray beam damage is uncommon for heterogeneous catalysis as for homogeneous catalysts and biological samples. In the study, the effect of high-flux X-ray on the Rh clusters and nanoparticles was highlighted along with providing mitigation strategies in order to reduce the damage caused by the high-flux X-ray beam. The second important finding is about the characterization of Rh clusters and nanoparticles during CO reduction treatment using DRIFTS. In this study, the integration of low-temperature CO oxidation kinetics as a characterization tool with DRIFTS, XAFS and scanning/transmission electron microscopy (STEM) was found to be necessary to improve the characterization of Rh single atoms. Implementing CO oxidation measurements at low temperatures can provide a rough estimation of the percentage of Rh single atoms. The third finding is related to the stability of Rh clusters upon exposure to CO, oxygen and CO oxidation at different temperatures. The study shows an unexpected dynamic structural change that the Rh cluster undergoes during exposure to oxygen even at room temperature in which the Rh clusters disperse to form Rh single atoms. This dispersion phenomenon was found to be size, gas environment and temperature dependent. For example, small clusters tend to disperse while large nanoparticles resist dispersion. additionally, increasing the temperature to ∼ 160 with CO and oxygen lead to an increase in the percentage of Rh single atoms. More importantly, the dispersed catalyst (Rh single atoms) exhibits higher CO oxidation activity than Rh nanoparticles by 350x. This finding can also be used for Rh single atoms synthesis for different oxide supports such as MgAl2O4, TiO2, and CeO2. Finally, the fourth finding is about investigating the CO oxidation kinetics and mechanism. The kinetics of Rh single atoms differ from Rh nanoparticles. Implementing in-situ spectroscopy helps to identify the resting state of the Rh complex during CO oxidation which is Rh(CO)2. By combining CO oxidation kinetics and in-situ spectroscopy, the plausible mechanism was suggested to be Eley-Rideal/Mars Van Krevelen mechanism.