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.

A vast set of methodologies exist for predicting and estimating future values from a time series dataset such as a stock market price data. Deep learning techniques such as Convolutional Neural Networks (CNN) and Long-short term memory (LSTM) neural networks have been applied to the stock price prediction problem. In this work, using the MSE (Mean Squared Error) as the performance measure, we compare the performance of LSTM to that of the CNN. Additionally, this work was quantitatively able to express the difference in performance between the LSTM and the CNN, this work was also able to give evidence to the fact that LSTMs in fact are better in predicting stock price data, given the constraints within the paper, compared to CNNs.

COVID-19 was a globalized pandemic and its impacts have affected most countries in a profound way. Not only were the healthcare systems thrown into chaos and lockdown have put many lives in pause but the economic impacts were strenuous as well. Although the economies of several countries were hurt similarly, the United States and China have a disparity between them. Prior to understanding the gaps between the economic states of these two countries, it is imperative to understand each of them in turn.

Cystic Fibrosis is the most common autosomal recessive disorder that affects Caucasians. It is caused by a mutation of the expression of the CF transmembrane conductance regulator (CFTR) gene, which has over 2,500 noted variants, spawning irregular chloride channels in mucus and sweat-producing cells. The disease causes damage to the respiratory system and GI tract until the organs fail to function by causing thick mucus to build up in the lungs and pancreas. As the chronic disease currently has no cure, people with this condition can generally live normal lives, as many tools and techniques can be used to manage this complex condition. However, this does not mean that patients live perfectly healthy lives, as they still experience symptoms such as shortness of breath and coughing. This review article summarises the pathology and pathomechanism of the disease, what it is on a genetic expression level, and emerging therapies.