Surface Freezing in Surfactant/Alkane/Water Systems

Surface freezing transitions in mixed monolayers of a homologous series of cationic surfactants, the alkyltrimethyl ammonium bromides (CnTAB where n = 12, 14, 16, 18), as well as a range of non-ionic, zwitterionic and biological surfactants, have been investigated ellipsometrically with a range of n-alkanes (Cm where m = 12 – 20, 28). Two distinct solid phases are observed depending upon the chain length difference between surfactant and n-alkane. Type I solid phases consist of a surface frozen mixed monolayer and are formed when this difference is small. Type II solid phases are bilayer structures with a frozen layer of neat n-alkane above a liquid-like mixed monolayer. Type II freezing was thought to occur via wetting of surface frozen n-alkane, as previously reported type II transitions took place in the presence of surface frozen n-alkanes. Thermodynamically stable type II solid phases have now been found in the presence of n-alkanes that do not show surface freezing at the air/alkane interface, however, and so this picture is incomplete. In the presence of pentadecane, for example, the biological surfactant lyso-OPC forms a stable type II solid phase 6.5 °C above the n-alkane bulk melting point. Such a large surface freezing range is unprecedented for a type II system. Studies using external reflection FTIR (ER-FTIRS) and vibrational sum-frequency spectroscopies (VSFS) have been used to probe these novel behaviours. Results were fully consistent with the proposed structures of both type I and type II surface frozen layers. 2D correlation analysis of ER-FTIR spectra as a function of temperature showed that type II frozen layer formation does not proceed via a simple wetting transition, with the formation of a transient intermediate implied. Evidence for such an intermediate was provided by dynamic ellipsometry measurements on the type II C18TAB/n-eicosane system

New Directions for Contact Integrators

Contact integrators are a family of geometric numerical schemes which guarantee the conservation of the contact structure. In this work we review the construction of both the variational and Hamiltonian versions of these methods. We illustrate some of the advantages of geometric integration in the dissipative setting by focusing on models inspired by recent studies in celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282

Groundwork for the Development of GPU Enabled Group Testing Regression Models

In this dissertation, we develop novel techniques that allow for the regression analysis of data emerging from group testing processes and set the groundwork for graphic processing units (GPU) enabled implementations. Group testing primarily occurs in clinical laboratories, where it is used to quickly and cheaply diagnose patients. Typically, group testing tests a pooled specimen--several specimens combined into one sample--instead of testing individual specimens one-by-one. This method reduces costs by using fewer tests when the disease prevalence is low. Due to recent advances in diagnostic technology, group testing protocols were extended to incorporate multiplex assays, which are diagnostic tests that, unlike their predecessors, test for multiple infectious agents simultaneously. The diseases that a multiplex assay screen typically share co-infection risks. The positive correlation stemming from co-infection risks creates a more challenging modeling framework. In this work, we develop a Bayesian regression methodology that can analyze multiplex testing outcomes collected as part of any group testing protocol. The model can maintain marginal interpretability for regression parameters and, when the assay accuracies are unknown, we can simultaneously estimate regression parameters with the test\u27s sensitivity (true positive rate) and specificity (true negative rate). Based on a carefully constructed data augmentation strategy, we derive a Markov chain Monte Carlo (MCMC) posterior sampling algorithm that can be used to complete model fitting. We demonstrate our methodology via numerical simulations and by using it to analyze chlamydia and gonorrhea, which are sexually transmitted infections, data collected as part of Iowa\u27s public health laboratory\u27s testing efforts. This regression framework\u27s drawback is the computational intensity of the proposed steps in the MCMC algorithm. Due to computational costs, this algorithm does not scale well to the high-volume clinical laboratory settings where group testing is commonly employed. We need to accelerate the proposed algorithms to provide for faster modeling fitting and prediction. The devised MCMC algorithm has several independent steps (e.g., sampling the latent disease status of each patient), or matrix operations. These sections of the algorithm are well-suited to be accelerated with parallel processing. Parallel processing is a software task that was historically used on large-scale computer clusters, but GPUs perform similar computations. To solve the scaling issue with our MCMC algorithms, we explore the GPU as a remedy. We discuss the necessary GPU techniques to take the first steps toward fitting group testing regression models with GPUs. Specifically, we explore stochastic gradient and coordinate descent algorithm implementation with independent steps and matrix operations. Finally, we provide an example MCMC implementation on a GPU to demonstrate potential acceleration

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

Neural Prosthetic Advancement: identification of circuitry in the Posterior Parietal Cortex

There are limited options for rehabilitation following an established Spinal Cord Injury (SCI) resulting in paralysis. For most of the individuals affected, SCI means a lifetime of confinement to a wheelchair and overall reduced independence. Brain-Computer and Brain-Machine Interface (BCI and BMI) techniques may be of aid when used for assistive purposes. However, these techniques are still far from being implemented in daily rehabilitative practice. Existing literature on the use of BCI and BMI techniques in SCI is limited and focuses on the extraction of motor control signals from the primary motor cortex (M1). However, evidence suggests that in long-term established SCI the functional activation of motor and premotor areas tends to decrease over time. In the present project, we explore the possibility of successful implementation of assistive BCI and BMI systems using posterior parietal areas as extraction sites of motor control activity. Firstly, we will investigate the representation of space in the posterior parietal cortex (PPC) and whether evidence of body-centered reference frames can be found in healthy individuals. We will then proceed to extract information regarding the residual level of motor imagery activity in individuals suffering from long-term and high-level SCI. Our aim is to ascertain whether functional activation of motor and posterior areas is comparable to that of matched controls. Finally, we will present work that was done in collaboration with the Netherlands Organisation for Applied Scientific Research that can offer an example of successful application of a BCI technique for rehabilitation purposes

Simultaneous modelling and clustering of visual field data

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIn the health-informatics and bio-medical domains, clinicians produce an enormous amount of data which can be complex and high in dimensionality. This scenario includes visual field data, which are used for managing the second leading cause of blindness in the world: glaucoma. Visual field data are the most common type of data collected to diagnose glaucoma in patients, and usually the data consist of 54 or 76 variables (which are referred to as visual field locations). Due to the large number of variables, the six nerve fiber bundles (6NFB), which is a collection of visual field locations in groups, are the standard clusters used in visual field data to represent the physiological traits of the retina. However, with regard to classification accuracy of the data, this research proposes a technique to find other significant spatial clusters of visual field with higher classification accuracy than the 6NFB. This thesis presents a novel clustering technique, namely, Simultaneous Modelling and Clustering (SMC). SMC performs clustering on data based on classification accuracy using heuristic search techniques. The method searches a collection of significant clusters of visual field locations that indicate visual field loss progression. The aim of this research is two-fold. Firstly, SMC algorithms are developed and tested on data to investigate the effectiveness and efficiency of the method using optimisation and classification methods. Secondly, a significant clustering arrangement of visual field, which highly interrelated visual field locations to represent progression of visual field loss with high classification accuracy, is searched to complement the 6NFB in diagnosis of glaucoma. A new clustering arrangement of visual field locations can be used by medical practitioners together with the 6NFB to complement each other in diagnosis of glaucoma in patients. This research conducts extensive experiment work on both visual field and simulated data to evaluate the proposed method. The results obtained suggest the proposed method appears to be an effective and efficient method in clustering visual field data and 3 improving classification accuracy. The key contributions of this work are the novel model-based clustering of visual field data, effective and efficient algorithms for SMC, practical knowledge of visual field data in the diagnosis of glaucoma and the presentation a generic framework for modelling and clustering which is highly applicable to many other dataset/model combinations

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum