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Permeable pavement structures (PPSs) are one of the significant LID systems that have potential positive effect on the ecosystem. Yet, the performance of permeable pavements is still questionable. Further studies on the hydrological performance of the system need to be addressed for better design criteria and maintenance during the operation. The infiltration through the pavement is a crucial parameter that projects the system performance. Several factors affect its deterioration. The entrapment of suspended materials associated with the infiltrated stormwater through the system is one of the major factors that affect its performance. Factors that promote the entrapment of particles were discussed thoroughly through the literature and are explained in this study. Many previous studies were focused on performing experimental work and developing empirical models to study the hydraulic performance of the system. Yet, prediction models on the infiltration deterioration need to be addressed and theoretical analysis needs to be performed in order to determine the empirical coefficients with defined parameters that were introduced in the previous literature. Furthermore, the sensitivity of the pore and particle size distribution and mass loading rate of the suspended materials on the infiltration rate need to be addressed. The study focuses on investigating performance of PPSs with examining the variation effect of pore and particle size distribution on it. A prediction model was made and simulated using Matlab software, in which pore and particle size means and standard deviations are taken as inputs. Further, the variation in these parameters on infiltration is examined. Critical levels, that infiltration decline would reach, were defined based on the introduced mechanisms from the previous literature. Based on the variation of pore and particle size means and standard deviations, these critical levels were studied through the analysis of the obtained results from the simulated model. / Graduate / 2019-12-10

There is growing interest in using large-scale observational data collected through national disease registries to develop clinical prediction models (CPMs) that use the experiences of past patients to make predictions about risks of outcome in future patients. CPMs are often developed in isolation across different populations, with repetitive de novo development a common modelling strategy. However, this fails to utilise all available information and does not respond to changes in health processes through time/space. Using the UK transcatheter aortic valve implantation (TAVI) registry as motivation, this thesis aimed to develop methods that improve the development of CPMs within local populations. Three research questions (RQs) were considered: (1) what are the challenges of mortality risk prediction in TAVI due to changes in procedure knowledge and the patient population? (2) Can we use a combination of baseline patient characteristics to predict the risk of mortality post TAVI? (3) How can we exploit multi-dimensional information about patients to inform clinical decision-making at a local-level? Chapter 2 demonstrates potential to simplify the procedure by removing pre-dilation of the aortic valve, thereby altering the underlying treatment pathway, and Chapter 3 shows that mortality rates from registries should be reported in the context of the underlying patient population. Despite Chapter 2 and 3 presenting potential challenges to TAVI risk prediction (RQ 1), CPMs are fundamental to support benchmarking/audit analyses. To this end, Chapter 4 found that the performance of existing TAVI CPMs was inadequate for use in UK patients. Through the discovery of new risk factors (e.g. frailty) in Chapter 5, the thesis derived a UK-TAVI CPM for audit analyses within the UK cohort (Chapter 6). While Chapters 4-6 present the classic framework of CPM development (RQ 2), this cannot overcome the challenges of mortality prediction in the TAVI setting (RQ 1) and is not suited to support local healthcare decision-making (RQ 3). Thus, Chapter 7 found that local model development could be supported through aggregating existing models rather than re-development. Existing methods of model aggregation were extended in Chapter 8 to allow prior research and new data to be utilised within the modelling strategy; application of the herein derived method to the UK TAVI registry indicated that it could facilitate the choice between model aggregation and de novo CPM derivation. Generally, this thesis has the potential to improve the implementation of CPMs within local populations by moving away from the iterative process of re-development. Practically, the thesis derived a UK-TAVI CPM for audit analyses, using classic and novel methodology.

Yes / Variable-displacement vane oil pumps are increasingly employed in automotive powertrains for their efficiency benefits through reduced losses. However, confirming long life reliability of a new commodity based on limited data available from product development tests and early field experience is a significant challenge, which is addressed by the work presented in this paper. The approach presented combines physical examination of pumps returned from tests, with analysis of damage factors for pump wear progression, and an analysis of functional parameters for the powertrain system focused on the cause effect linkages across the systems hierarchy. The metrology results from physical measurements of used parts provide useful insights for the wear progression and the expected service performance of the pump. The paper also expands towards a data driven approach based on ECU data analysis that could provide a pathway towards the development of online health monitoring and diagnostics of the oil pumps. / Research project: “Intelligent Personalised Powertrain Health Care”, funded by Jaguar Land Rover.

Grain nitrogen (N) concentration is a major quality criterion of malting barley for which there is a narrow range that producers must meet to satisfy market requirements (1.52 – 1.84 %). In recent years growers in Ireland have had difficulty producing grain with a high enough N concentration to meet these requirements using standard recommended agronomic regimes. The reasons for the lower than expected grain N concentrations are not known. There is interest from growers and maltsters in the development of a system to forecast likely grain N concentration from crop measurements made at or before flowering. A forecasting system would allow growers to identify crops at risk of falling below specification and to apply late N fertiliser if needed. It would also enable maltsters to plan grain intake and malting operations in advance of harvest. The aim of this project was to investigate the potential for predicting grain N concentrations of spring barley from crop and soil measurements made at ear emergence. The main objectives were to 1) investigate the relationships between measurements made at ear emergence and grain N concentration at harvest in order to identify which characteristics should be included as variables in multiple regression models to explain variation in grain N concentration, 2) to use the models with independent data sets to predict grain N concentration and test the accuracy of the predictions, 3) to quantify the recovery by the crop of fertiliser N applied at anthesis and its effects on grain N concentration and 4) to determine whether non-destructive techniques can provide estimates of crop growth and N content for use in the prediction models. Field experiments were established with plots of spring barley (Hordeum vulgare cv. SY Taberna) at one site in 2013 and two sites in 2014 representative of those employed in malting barley production in Ireland. Fertiliser N applications were varied over the range 0 – 210 kg N/ha (with dressings split between sowing and mid-tillering) to provide a range of crop growth and grain N concentrations. In some experiments additional applications of N were made at anthesis to quantify effects on grain N concentration and seed rate treatments (150, 300 and 600 seeds per m-2) imposed to test the accuracy of predictions of grain N concentration in crops of varying canopy structure. Destructive samples were taken to determine total crop N content and canopy N distribution at ear emergence and harvest. Measurements of soil mineral N availability, ear numbers per m-2 and the number of spikelets per ear were made at ear emergence. Final grain yield and quality were also determined at harvest. Grain N concentration is the quotient of grain N content and grain yield. Both grain N content and yield explained a significant amount of the variation in grain N concentration observed across sites and fertiliser N treatments indicating that estimates of both must be included in models to predict N concentration. Grain N content was strongly related to total crop N content at harvest (P < 0.001; R2 = 0.96), which in turn was related to canopy N content at ear emergence (P < 0.001; R2 = 0.94). Similarly, grain yield was strongly related to total crop biomass at harvest (P < 0.001; R2 = 0.83), which in turn was related to crop biomass at ear emergence (P < 0.001; R2 = 0.88). These results indicated that predictions of grain N concentration might be possible from measurements of crop N content and biomass at ear emergence and that the effects of variation in harvest index, nitrogen harvest index and post-anthesis N uptake on grain N concentration are likely to be negligible under normal agronomic conditions in Ireland. Weather conditions in 2013 were unusually dry and estimates of soil moisture deficit and available water capacity indicated that the crop was water stressed. In 2014 weather conditions were close to the long term averages for the sites. Multiple regression models using canopy N content and biomass at ear emergence as explanatory variables accounted for 91% of the variation in grain N concentration when data from 2014 were used and 80% when data from both 2013 and 2014 were combined. The models developed using data from plots sown at 300 seed per m-2 in 2014 were tested against independent data from plots sown at 150 seeds per m-2 in the same year and at the same sites to test the accuracy of predictions across plant populations and canopy structures. The models were also tested using data from experimental plots and commercial fields collected in 2015 to test the accuracy of predictions in a different year across a range of sites and varieties. Values of grain N concentration predicted from measurements at ear emergence were compared with actual grain N concentrations measured at harvest. The accuracy of predictions was good with an R2 of 0.80 and RMSE of 0.114 %N for the test across seed rates and R2 of 0.80 and RMSE 0.220 %N for the validation in 2015 across sites and varieties. In 2014, grain N concentrations were increased significantly by applications of additional N fertiliser at anthesis with apparent recoveries (increase in N content (kg) /kg fertiliser N applied) in grain averaging 50% over the range of application rates indicating scope for increasing grain N concentration in crops predicted to be at risk of not meeting malting specifications Non-destructive measurements displayed significant relationships with N content and biomass at GS 59 across a combination of sites and seasons. However, issues in performance relating to instrument saturation were obvious and estimates never produced more accurate predictions of grain N concentration than destructive sampling. The results show that grain N concentration of spring barley can be predicted with good accuracy from measurements of canopy N and crop biomass made at ear emergence when the weather conditions are comparable to the long term average for the region. As conditions of drought are rare in Ireland, the prediction models are a potentially valuable tool to aid crop management and post-harvest operations by growers and maltsters. Further testing will be needed before users can be confident in the reliability of predictions over years and a larger set of varieties.

End-gated and centre gated mouldings have been assessed with varying thickness and sprue geometries for the centre gate. Alternative image analysis techniques are used to measure the orientation and length of injection moulded short and long fibres composite components. The fibre orientation distribution (FOD) measurements for both geometries have been taken along the flow path. In shear flow the FOD changes along the flow path, however the FOD remains relatively constant during expansion flow. The core width and FOD at the skin within a long glass fibre (LGF) specimen is different in comparison to a short glass fibre (SGF) specimen. Fibre length measurements have been taken from the extrudate, sprue and 2 positions within the centre gate cavity. The size of the sprue has little influence on fibre breakage if the moulding is more than 1 mm thick The SGF FOD prediction models within Autodesk Simulation Moldflow Insight 2014 (ASMI) have been validated against measured SGF data. At present, by default, the models over-predict the < cos2θ > for most geometries. When the coefficients are tailored for each model, drastic improvements are seen in the FOD prediction. The recently developed SGF RSC model accurately predicts the FOD in shear, in a thin geometry, whereas the Folgar-Tucker model predicts the FOD accurately in expansion flow. The measured LGF fibre length distribution (FLD) and FOD have been validated against the LGF prediction models. The LGF models are currently under predicting the breakage and over-predicting < cos2θ >. The breakage prediction improves if measured FLD of the extrudate is input into the model.

The majority of drugs available on the market are intended to be administered through the oral route. To achieve the desired therapeutic effect, an orally administered drug must first reach the systemic circulation and then its site of action. The fraction of the administered drug that reaches the systemic circulation is known as oral bioavailability and it is the product of the absorption and first-pass metabolism processes occurring in both the GI tract and the liver. The factors controlling bioavailability are manifold –both drug and physiologically related - and their complex interplay is key to defining a drug’s oral bioavailability. In drug discovery and development it is therefore pivotal to anticipate and understand the bioavailability of a drug candidate; a far from simple task, considering the multifactorial nature of the process. For that reason, the overall aim of this thesis was to provide different modelling and simulation (M&S) strategies that can be used for the prediction of oral bioavailability that can be of use in drug discovery and development. The first part of this thesis was focused on the evaluation of the use of bioavailability data obtained from pre-clinical species as a predictor of the human value, in a more traditional approach. In particular, the aim was to evaluate models that can quantitatively and qualitatively provide a relationship between animal and human bioavailability, by analysing trends in a large bioavailability dataset. This section demonstrated that although pre-clinical species cannot quantitatively predict bioavailability, the data obtained from them can be used for qualitative prediction of the human value. Nevertheless, such a modelling approach does not provide a mechanistic rationale of the factors affecting the bioavailability differences. Consequently, the second part of this thesis was focused on such mechanistic predictions. Particularly, we investigated the impact that drug release patterns can have on drug absorption and intestinal first pass metabolism, taking into account the physiological differences observed across the length of the human gastrointestinal (GI) tract. These release patterns are suspected to lead to bioavailability differences due to changes in the first-pass metabolism, especially for CYP3A substrates. Therefore we investigated this phenomenon applying a physiologically-based pharmacokinetic (PBPK) M&S approach: firstly, from the discovery point of view, using PBPK models in a prospective fashion to investigating the drug-related factors that might lead to such differences and secondly, from the development point of view, to predict the mechanistic differences in absorption and metabolism of oxybutynin, a drug known for its higher bioavailability when formulated as a modified release (MR) product. The latter was done by developing and applying a novel simplified PBPK model to predict such differences. The results of this work showed that the intestinal metabolism can be significantly reduced when having MR formulations of CYP3A substrates which, in some cases, can lead to higher relative bioavailability. Additionally, this thesis provided novel methods and models that have the potential to improve bioavailability predictions when using PBPK models, in particular for drugs formulated as MR.

End-gated and centre gated mouldings have been assessed with varying thickness and sprue geometries for the centre gate. Alternative image analysis techniques are used to measure the orientation and length of injection moulded short and long fibres composite components. The fibre orientation distribution (FOD) measurements for both geometries have been taken along the flow path. In shear flow the FOD changes along the flow path, however the FOD remains relatively constant during expansion flow. The core width and FOD at the skin within a long glass fibre (LGF) specimen is different in comparison to a short glass fibre (SGF) specimen. Fibre length measurements have been taken from the extrudate, sprue and 2 positions within the centre gate cavity. The size of the sprue has little influence on fibre breakage if the moulding is more than 1 mm thick The SGF FOD prediction models within Autodesk Simulation Moldflow Insight 2014 (ASMI) have been validated against measured SGF data. At present, by default, the models over-predict the <cos2θ> for most geometries. When the coefficients are tailored for each model, drastic improvements are seen in the FOD prediction. The recently developed SGF RSC model accurately predicts the FOD in shear, in a thin geometry, whereas the Folgar-Tucker model predicts the FOD accurately in expansion flow. The measured LGF fibre length distribution (FLD) and FOD have been validated against the LGF prediction models. The LGF models are currently under predicting the breakage and over-predicting <cos2θ>. The breakage prediction improves if measured FLD of the extrudate is input into the model. / Autodesk Ltd.

The full text will be available at the end of the embargo period: 4th Aug 2025