Published Papers

This study addresses the pressing need for an efficient and accessible method to dry and sanitize masks, particularly for economically disadvantaged individuals. Through a meticulous development process, a prototype was engineered employing a combination of UV light and hot air within a perspex chamber. The design prioritizes safety, efficiency, and user-friendliness, targeting the working-class population. Performance testing demonstrated the prototype's effectiveness in thoroughly drying and sanitizing masks within the designated time frame. Feedback from both user surveys and expert appraisal provided valuable insights, highlighting areas for refinement. Weaknesses identified, including paint finish, cable arrangement, size, and technical complexity, informed proposed improvements. Additionally, suggestions for a comprehensive tutorial and potential collaboration with resource conservation centers were outlined to enhance dissemination of the product's construction knowledge. Overall, this innovative prototype shows promising potential in addressing mask sanitation challenges, with room for further refinement and widespread implementation in a post-pandemic landscape.

Findings on EPR spectroscopy applied to DPPH and Manganese Chloride matched the initial hypothesis in line with the currently accepted theories on Electron Spin Resonance (ESR). The spectrometer instrumentation and apparatus in the experiment was intricate, The Varian E-3 X-band spectrometer was used. The g-values were calculated with a simplified formula that included the variables of Electromagnetic wave frequency, scalar constant and the center field. Some differences and minor anomalies were observed when comparing the expected theoretical values with the experimental values obtained, however, the final experimental values were quite similar to that of the theoretical g-values, thus proving the experiment a success

Different aspects of orbits of celestial bodies can be calculated by using different formulae. In this paper it is proposed that iterating over some initial conditions, using only one formula, many other phenomena of orbits, such as the variation in speed during the orbit, can be simulated. By calculating only the force between two bodies for some number of discrete time steps, it is possible to compute velocities, kinetic energies, and potential energies for the particles in question which match with the expected behaviour as predicted by other formulae. This study uses such a simulation to simulate the orbits of bodies with different initial conditions and then compares then analyses their behaviour with respect to quantities like kinetic and potential energies, values which the simulation is not programmed to take into account but does so anyway as an emergent effect. The study thereby validates this method of simulation of forces through iteration with discrete time steps.

This experiment investigates the efficiency and therefore viability of hydroponic farming in comparison to traditional farming through building an at-home hydroponic farm. The aim of the experiment was to test if hydroponic farming could be used on an industrial scale and whether it could potentially replace soil-based farming. It measures the growth rate of plants in hydroponic and in traditional conditions by making one farm of each type. Four different plants: carrots, garlic, mint, and spring onions are grown in each farm under constant conditions- the sunlight, temperature, care given etc.- and are regularly measured and compared. The experiment finds that plants generally grow more efficiently in hydroponic conditions, however, geophytes do not grow well hydroponically compared to traditionally, likely due to overwatering. It also finds that hydroponic systems are easier to care for than traditional systems.

The January effect is perhaps the most extensively researched calendar anomaly in finance. However, it is still largely considered to be a mystery. This empirical study will review the extensive body of research surrounding the January effect and will endeavour to answer questions relating to why the January effect exists, as well as questions relating to its consistency. More specifically, it will look over the presence of the January effect globally, identifying potential trends across time and region. This paper will also take a look at the most popular explanations to the January effect, including taxloss selling and window dressing, verifying their validity. It will also consider other explanations that have not garnered as much popularity, such as performance hedging, seasonal liquidity, and investor optimism. This paper will however not be conducting any independent data examination or econometric analysis.

Machine learning (ML) has revolutionized healthcare and medicine by enabling data-driven insights and decision-making. This paper focuses on deploying ML in healthcare, particularly for diseases like diabetes, to improve predictability using various algorithms and metrics. It highlights the importance of data quality, diverse datasets, and appropriate algorithms for building reliable diagnostic systems. The paper discusses experiments and evaluation metrics, including accuracy, precision, recall, and F1-score, to assess model performance. The study emphasizes the potential of ML in diabetes management, predicting blood glucose levels, aiding insulin dosage adjustments, and detecting complications early. It also discusses the need for data privacy, model interpretability, and ethical considerations in ML healthcare applications.