Cybersecurity is an ever-expanding field. In order to stay current, training, development, and constant learning are necessary. One of these training methods has historically been competitions. Cybersecurity competitions provide a method for competitors to experience firsthand cybersecurity concepts and situations. These experiences can help build interest in, and improve skills in, cybersecurity. While there are diverse types of cybersecurity competitions, most are run with on-premise hardware, often centralized at a specific location, and are usually limited in scope by available hardware. This research focuses on the possibility of running cybersecurity competitions, specifically CCDC style competitions, in a public cloud environment. A framework for running cybersecurity competitions in general was developed and is presented in this research. The framework exists to assist those who are considering moving their competition to the cloud. After the framework was completed, a CCDC style competition was developed and run entirely in a public cloud environment. This allowed for a test of the framework, as well as a comparison against traditional, on-premise hosting of a CCDC. The cloud-based CCDC created was significantly less expensive than running a comparable size competition in on-premise hardware. Performance problems—typically endemic in traditionally-hosted CCDCs—were virtually non-existent. Other benefits, as well as potential contraindications, are also discussed. Another CCDC style competition, this one originally built for on-premise hardware, was then ported to the same public cloud provider. This porting process helped to further evaluate and enrich the framework. The porting process was successful, and data was added to the framework.

Poly-aryl, also known as polyphenylene compounds are a class of dendrimer, which contain a large number of aromatic rings. They are of interest because they display restricted rotation of their stearically congested aromatic rings. These extended structures have the potential to act as precursors for even larger aromatic systems and have many applications including electronic devices, drug delivery and catalysis. A total of 23 novel poly-aryl compounds have been examined using single crystal X-ray diffraction and a number of structural patterns have emerged. Six of the compounds contain alkynes and it has been observed that their conformation is governed by a combination of conjugation between the alkyne and aryl groups and inter-molecular interactions. In the more extended poly-aryl compounds steric congestion rules out any possibility of conjugation between the rings and their conformation is governed by intra-molecular non-bonded interactions in the core of the molecules and by inter-molecular interactions in their periphery. Where possible, solution NMR measurements were carried out on the poly-aryl compounds and confirmed that the solution structures are in agreement with those obtained from individual crystal.

Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).