The term artist can be defined as ‘a person who creates paintings or drawings as a profession or hobby’ (Oxford Languages, 2022). I argue that that the Japanese contemporary artist, Takashi Murakami, redefines what it means to be an artist as his artistic practice successfully permeates all areas of the spectrum from “high” to “low” culture - from academic painting and the fine art industry to fashion, food, and even the Pop entertainment industry; essentially pushing the limits of what one artist may achieve during his lifetime, a success exemplified in his creation of the complex Superflat movement.He also maintains the core value of reflecting the complexities of the globalised world, as his Superflat style is famously influenced by his identity as a Japanese artist who moved to America, reacting to the cultural shock. In this essay I will explore significant factors in his life which have contributed to his artistic career, in order to discuss Murakami’s evolution of what it means to be an artist in the 21st Century.

This review article focuses on the field of epigenetics. Since its inception in the 20th century, there have been several major developments which induced the change in what the word “epigenetics” actually meant. In this review, the discoveries made so far are summarised and the potential applications of these new discoveries are discussed. Some things to keep in mind are that the field of epigenetics is relatively young compared to many other fields of research, so there are bound to be areas in which the understanding of the topic is lacking. Despite this, and partly due to recent advances in technology, several epigenetic therapeutics, some of which have already been approved for use, have been developed.

Wastewater treatment plants play an important role in maintaining the health of ecosystems and ensuring the economic, social, and political soundness of communities. However, current wastewater treatment methods are economically and environmentally unsustainable. Aquatic plant restoration has been receiving attention because of its high efficiency and eco-friendliness compared to previous methods. Three types of aquatic plants: duckweed and hornworts were tested for their effects in removing constitutes and reducing the number of bacteria colonies in a wastewater source, over the course of 7 days. The results show that all three aquatic plants were capable of recovering and removing bacteria in water. Duckweed, however, was the most effective of all three plants. A 3D digital model of a duckweed based wastewater treatment plant was devised to showcase how duckweed could be incorporated into full-scale water treatment systems. The uptake of aquatic-plant based wastewater treatment systems has been slow. The conducted research adds to the advantages and the feasibility of full-scale aquatic-plant based wastewater systems.

With the current landscape of MOSFETs in mind, the approaching limits and shortages of the modern silicon transistor has lead to many concerns over the future of semiconducting technology. This paper aims to look through one possible successor to the long-standing Silicon based MOSFET architecture in the form of Carbon Nanotubes. Although a lot has been researched about the wonder material graphene, many research have not yet reached solid conclusions about the potential of graphene and carbon’s allotropes. One example is the Standford model of a 16-bit RISC-V architecture microprocessor that sets a precedent for the earliest prototypes of a CNT based system. A following prototype designed by MIT engineers also prove the possibility of CNT processors. In the time of writing this paper, no other substantial project on CNT processor have been conducted leaving much room for future findings and improvements to existing models.

Trend-following strategies (TFS) have been well-established for their effectiveness in analysing stock prices for decades. However, there remains a pressing need to revisit and analyse their performance in today’s increasingly volatile financial environment. First, this study investigated their profitabilities with respect to the S&P500 fund over the past 10 years. The fund’s consistent and strong uptrend over the 10-year period resulted in TFS be ingunable to outperform the passive buy-and-hold strategy. Longer moving averages and breakout lengths were more profitable given the fund’s bullish nature. Additionally, it was found that exponential moving averages were more effective than simple moving averages. The study also established that trading more frequently, such as daily, had no advantage over trading weekly or monthly. TFS incorporated with stop losses were largely ineffective and were only profitable when market prices displayed strong and consistent trends. Second, this study examined the relevance of TFS in varying economic climates by using data across various market sectors and time periods. It was found that TFS performed better when prices display both bullish and bearish trends as opposed to when prices only trend in one direction or experience frequent fluctuations. Given the steady uptrend in the S&P500 fund in recent times, the effectiveness of these strategies have deteriorated compared to the past where price patterns were less consistent.Thus, it can be said that the relevance of TFS have diminished for funds displaying consistent one-directional trends,like the S&P500 fund, or extremely volatile price patterns.

The purpose of this study was to determine if there was a significant difference in the performance of an Artificial Neural Network (ANN) and a Support Vector Machine (SVM) for liver cancer classification. The reason to determine if there is a significant difference is to find an artificial intelligence model that can best classify liver cancer images in order to prevent future cases of liver cancer in patients. The performance of both models was compared and validated on the LiTS – Liver Tumor Segmentation Benchmark (LiTS17) dataset in terms of accuracy. The comparative results show that the SVM classifier outperforms the ANN classifier where SVM gave an classification accuracy of 82.83% whereas the ANN gave an classification accuracy of 63.48%. This result indicates that the classification capability of SVM is better than ANN and may potentially fill in a gap in the use of current or future classification algorithms for liver cancer.