Published Papers

This paper investigates the impact of the Christmas period on the prices of five cryptocurrencies Bitcoin, Ethereum, Ripple, Litecoin and Dogecoin from 2017 to 2021 By employing statistical and data science analysis, the study aims to enhance the understanding of seasonality anomalies in the cryptocurrency market The results reveal significant price fluctuations during the Christmas period, highlighting the potential for seasonal effects in digital currencies.

In this paper, I analyze the relationship between infrastructure investment, environmental degradation and. I first present their theoretical link through the lens of the theory of externalities; in particular, investment in infrastructure is associated with a positive externality since infrastructure is a public good, but both the usage and the construction of infrastructure pose negative environmental externalities. I synthesize the relatively disparate literature on this topic, before jointly estimating these externalities using cross-country data. I find that a 1% increase in infrastructure investments is associated with a 1.04% increase in average GDP, and a 0.77% increase in greenhouse gas (GHGs) emissions. I find that a 1% higher tax rate on energy dampens the association of such investments on GHGs.

The relationship between income inequality and economic growth has been the subject of extensive theoretical debate, with varying predictions on whether inequality fosters or hinders growth. This study investigates the relationship between income inequality and economic growth using a fixed-effects model, estimated on cross-country data spanning from 1963 to 2015. The findings suggest a robust negative correlation between income inequality and economic growth, with stronger effects observed in more developed countries

Space is an extreme environment, which can heat and cool rapidly. Therefore, satellites that must function in this harsh climate for prolonged periods can sustain damage to equipment and wiring if the isolated system of the satellites is not temperature regulated [1]. Therefore, all satellites launched into orbit in space have some form of the thermal control system, which ensures that the satellite is regulated at an ideal temperature for payloads [2] that work at lower temperatures and to prevent damage caused to physical structures, misalignment of optical systems etc. due to large temperature differences. Generally, two types of thermal control systems are integrated into satellites: active and passive systems. Active systems being thermal control systems that use moving fluids and mechanisms [3]. Conversely, passive methods do not have any mechanically moving fluids or parts [4]. The purpose of this study is to compare active and passive thermal control systems and assess their effectiveness for satellite applications through a case study of the Terra satellite. This study highlights the importance of efficient thermal management in satellites, balancing cost, energy consumption, and reliability for longterm missions.

Machine learning algorithms are employed on a football data set to forecast whether a shot results in a goal. Results from existing models are improved upon by employing various additional algorithms. At the initial stage, the study gathered data from an existing Kaggle article published online by Usama Waheed. The data set includes data from various leagues and their matches. The research analysed shot events, filtered out non-relevant events, and created features based on shot coordinates, angles, player skill, and shot type. Nine machine learning algorithms were implemented: logistic regression, XGBoost, random forests, support vector machines, k-nearest neighbours, decision trees, LightGBM, CatBoost, and artificial neural networks. The research also focused on removing the redundancy, optimizing performance on imbalanced datasets, and fine-tuning model hyperparameters by employing nested cross-validation. Lastly, the models were evaluated on accuracy, precision, recall, and training time metrics. The results revealed insights into the strengths and weaknesses of each model in predicting goals, with specific emphasis on areas of improvement for shot-based football analytics. Decision trees produced the most accurate predictions on the test set.

This paper investigates the extent to which the use of generative AI (Artificial Intelligence) poses ethical dilemmas in political campaigns. By reviewing the newest literature on the topic, this paper examines the problems associated with the creation of ethics guidelines on AI-generated content (AIGC) use. In pre-existing guidelines, certain vocabulary such as “transparency,” “dignity,” and “freedom & autonomy” were most frequently repeated. This shows not just how prevalent these ethical issues are but also that proposed guidelines can become repetitive whilst AI's ethical dilemmas still have to be dealt with. With the analysis of case studies in France, India, and the US (United States) we can see the impacts of AIGC on political elections within countries with diverse levels of AIGC regulation. France and India have looser regulations on AIGC - but the US, whilst lacking regulation in some states, has adopted the strictest measures in others. In the absence of harmonized legislation, international ethical guidelines have a key place in the discussion. This paper addresses the possibilities of incorporating AI systems that flag negative and unethical social media posts. The paper suggests a direction for private and public institutions to integrate new beneficial AI-based countermeasures in future political campaigns to mitigate harm. By combining these measures with bringing more attention to the creation of guidelines surrounding AIGC, ethical dilemmas can be lessened.