This project aims to identify the key causes and barriers to chaos in a dynamical system. It will start with definitions of various concepts to provide support for the arguments and try to setup boundaries to what chaos is and isn’t. Furthermore, numerical analysis will be used as the equations are hard to solve analytically and graphic representations are useful. To make what is explained more tangible, this project will focus on the single and double pendulum.
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The pendulum is a Hamiltonian system without the dissipation of energy meaning it is simple enough to run simulations while also giving rise to interesting behaviour. Two different constructions of the pendulum will be explored as their motion are vastly different. The simple pendulum is ordered and often seen in pre-university textbooks, whereas the double pendulum is the prototypical example of a chaotic system. Therefore this project will explore from the perspective of these two systems, the factors which prevent chaos and those which lead to chaos.

Floods are widely considered the most expensive natural disaster due to their destructive nature and severe societal repercussions. Thus, the ability to predict flood occurrences and
gauge their socio-economic impacts is highly essential. To do so, this research collated the largest dataset of historical floods ever used in Australian flood research and mapped flood locations in the past two decades to raster datasets of hydrological flooding factors created using ArcGIS software. The resulting dataset underwent unsupervised clustering to display statistically significant and was used to train a deep learning neural network with an 85-90% accuracy.

In the following work, we will discuss a novel, integrated computational and synthetic approach for the rapid identification of N-heterocyclic drug-like small molecules that regulate 5-Hydroxytryptamine2A (5-HT2A). With the advance of computing power in the last few decades, it has become possible to utilise virtual libraries for the more efficient discovery of drug candidates towards specific targets. Developing a selective drug towards the 5-HT2A receptor has significant potential towards getting at therapeutics that can alleviate neurological disorders, such as depression, anxiety, and PTSD. Nitrogen heterocycles are interesting scaffolds in that they are widely prevalent in pharmaceutical drugs and natural products, and so, within this work, we worked to discover and synthesise molecules of this sort to target 5-HT2A.

Since the beginning of the COVID-19 outbreak in 2019, over 213 countries and territories have been affected, resulting in 668 million cases and 6 million deaths. Consequently, government responses instigated a nosedive in the global economy, particularly heightening gender-based economic inequality. This paper examines how women and men were economically impacted by the COVID-19 pandemic in China and New Zealand. Through a secondary data analysis of the female and male unemployment rates in China and New Zealand, findings suggest that women and men in China were less initially impacted by unemployment during the COVID-19 pandemic, but were faster to recover in New Zealand. Possible factors of gender-based economic inequality in respective countries include oppressive gender role attitudes, gender-biased media, and stratified workplaces. Reforms that must be considered or enacted include: employer or state-funded childcare or tax policies that encourage both spouses to work; radical reforms to changing childcare gender norms; family-friendly policies during pandemics; reduction of the time women spend in low-income unpaid work.

Recent studies have concentrated on bilingual people worldwide. However, a complete knowledge of the differences in executive function between bilinguals and monolinguals is still lacking. The argument over the bilingual advantage has persisted over the years despite the fact that existing research has only scratched the surface of the executive function skills that bilinguals perform better than monolinguals at. In order to examine the significance of executive function performance between bilinguals and monolinguals, the specific executive functions and kinds of bilingualism are discussed here. There are intriguing questions about the relationship between the two factors for the majority of bilingual speakers in this world, despite the fact that a different environment and diverse language experience can shape the level of bilingual proficiency, potentially influencing the performance of executive functions.

Nowadays, with the rapid development of Artificial Intelligence (AI) in our everyday lives, machine learning algorithms have been applied in a wide variety of fields, performing tasks that are unfeasible for conventional algorithms.Neural networks, inspired by the design of the biological nervous system, have become increasingly popular in fields that require recognizing relationships between vast amounts of data, such as facial recognition, stock market prediction, and signature verification. Different types of models each have their unique architectures. There are differences between the computational complexities of the models, influencing their performance of specific tasks. However, determining which architecture best suits a specific application, such as image classification, takes time and effort. This article compares the performance of two types of artificial neural networks when classifying images: the Convolutional Neural Network and the Fully Connected Neural Network.The results from experimentation lead to a better understanding of the two fundamental models and how their training and validation accuracies vary individually, both reaching a terminal point ultimately. It was clear that CNN had an advantage in terms of better accuracy but more time-consuming. It was also found that the number of convolutional layers does not necessarily improve the accuracies.