Expect the Unexpected: Deciphering Exoplanetary Signals with Machine Learning Techniques

The field of exoplanets has enjoyed unprecedented growth in the past decades, planets are being discovered at an exponential rate. With the launch of next-generation facilities in the coming decades, the arrival of high-quality spectroscopic data is expected to bring about yet another revolutionary change in our understanding of these remote worlds. The field has been actively developing tools to comprehend the large stream of incoming data, and among them, Machine Learning techniques are building up momentum as an alternative to conventional approaches. In this work, I developed methodologies to uncover potential biases in the interpretation of the exoplanetary atmosphere introduced during data analysis. I showed that naively combining observations from different instruments might lead to biased results, and in some extreme cases like WASP-96 b, it is impossible to com- bine observations. A new scheme of retrieval framework, namely the L - retrieval, holds the potential to detect incompatibility among different datasets by combining light-curve fitting with atmospheric radiative transfer modelling. This work also documents the application of ML techniques to two distinct fields of exoplanetary science: a planet signal detection pipeline for direct imaging data and a suite of diagnostic tools designed for the characterisation of exoplanets. In both approaches, I pioneered the integration of Explainable AI techniques to improve the reliability of the deep learning models. Initial successes of these novel methodologies have provided an exciting prospect to tackle upcoming challenges with the use of Artificial Intelligence. How- ever, significant work remains to progress these models from their current proof-of- concept stage to general application framework. In this thesis, I will discuss their current limitations, potential future, and the next steps required

Russian perspectives of online learning technologies in higher education: An empirical study of a MOOC

There has been a rapid growth of massive open online courses (MOOCs) in the global education market in the last decade. Online learning technologies are becoming increasingly widespread in the non-formal education sector and in higher and supplementary vocational education. The use of MOOCs in Russia to support the delivery of educational programmes at university level opens opportunities in terms of expanding the educational choice for students, the development of virtual academic mobility, reduction in the cost of educational services, and improvement in the accessibility of education. However, the effectiveness of using different online learning technologies at university level, and the consequences of their widespread adoption, has not been sufficiently explored. In this research study, a comparative analysis is made of the effects of different online learning models on student educational outcomes in a university setting. A study was undertaken in which different groups of students at the Ural Federal University, Russia, were encouraged to study technical and humanities disciplines using a framework of blended learning, and online learning with tutoring support. The results of the study were compared with the results of a reference (control) group of students who studied the same disciplines in a traditionally taught model. It was found that both models (blended and online) of MOOC implementation demonstrated greater learning gains, in comparison with the traditional model. For engineering and technical disciplines, there was no statistically significant difference between blended or online learning technologies. For the humanities discipline, where the communicative component of the learning process was significant, the blended learning technology produced better results. Conclusions of this empirical research may be useful for heads of educational organizations and teachers in helping them to make strategic decisions about the modernization of university courses by increasing the effectiveness of the implementation of new educational technologies. The results of this research project will be used for implementing the State Priority Project, ‘The Modern Digital Educational Environment of the Russian Federation’

Confidence intervals for reliability growth models with small sample sizes

Fully Bayesian approaches to analysis can be overly ambitious where there exist realistic limitations on the ability of experts to provide prior distributions for all relevant parameters. This research was motivated by situations where expert judgement exists to support the development of prior distributions describing the number of faults potentially inherent within a design but could not support useful descriptions of the rate at which they would be detected during a reliability-growth test. This paper develops inference properties for a reliability-growth model. The approach assumes a prior distribution for the ultimate number of faults that would be exposed if testing were to continue ad infinitum, but estimates the parameters of the intensity function empirically. A fixed-point iteration procedure to obtain the maximum likelihood estimate is investigated for bias and conditions of existence. The main purpose of this model is to support inference in situations where failure data are few. A procedure for providing statistical confidence intervals is investigated and shown to be suitable for small sample sizes. An application of these techniques is illustrated by an example

Technical Change Theory and Learning Curves: Patterns of Progress in Energy Technologies

This paper presents a comparative analysis of energy technology learning and progress within the framework of Schumpeter’s invention-innovation-diffusion paradigm. We estimate learning by doing and research rates for a range of energy technologies in four stages of technical progress. Emerging and mature technologies respond slowly to research and development (R&D) and capacity expansion; evolving technologies exhibit high learning-by-doing and research rates; reviving technologies exhibit considerable response to learning-by-research although they do not face significant market constraints. We generally find higher learning-by-doing than learning-by-research rates but do not find any development stage where learning-by-doing alone is the dominant driver of technical change. Also, high capital intensity and market constraints appear to slow down the pace of progress of emerging and evolving technologies. We find little scope for potential substitution between learning-by-doing and learning-by-research across the technologies and different stages of their development path

The Effects of Self-Reinforcing Mechanisms on Firm Performance

This study empirically investigates the influence of the market-bound (i.e., interaction and network effects) on the firm-bound (i.e., scale and learning effects) self-reinforcing mechanisms, and their combined effect on product and organizational performance. The findings from a sample of 257 manufacturing firms reveal that interaction effects have a positive effect on network effects. Network effects have a positive impact on the potential for firms to realize scale and learning effects, which in turn, is positively related to their actual realization of these effects. The actual realization of scale and learning effects has a positive effect on product performance, which in turn positively influences organizational performance. These effects are robust across industries and provide ample opportunities for future research.management;economics;increasing returns;self-reinforcing mechanisms

Cost-benefit modelling for reliability growth

Decisions during the reliability growth development process of engineering equipment involve trade-offs between cost and risk. However slight, there exists a chance an item of equipment will not function as planned during its specified life. Consequently the producer can incur a financial penalty. To date, reliability growth research has focussed on the development of models to estimate the rate of failure from test data. Such models are used to support decisions about the effectiveness of options to improve reliability. The extension of reliability growth models to incorporate financial costs associated with 'unreliability' is much neglected. In this paper, we extend a Bayesian reliability growth model to include cost analysis. The rationale of the stochastic process underpinning the growth model and the cost structures are described. The ways in which this model can be used to support cost-benefit analysis during product development are discussed and illustrated through a simple case