Management of newborns with disorders of sex development (DSD), especially in deciding the need for a sex assignment surgery, is a complex matter. It is associated with many bioethical issues, such as concerns about the rights and welfare of the newborns and the reliability of parents' consent to the paternalistic disposition of physicians in making the best decisions. This paper, containing interviews with six medical experts and three religious' experts, aims to raise awareness of the multidisciplinary approach, which uses a combination of medicine, religion, and ethics in managing children with DSD, particularly in Malaysia, to avoid unnecessary psychological, biological, emotional, and societal ramifications.
In light of developing and industrialized nations, the G20 economies account for a whopping two-thirds of the world's population and are the largest economies globally. Public emergencies have occasionally arisen due to the rapid spread of COVID-19 globally, impacting many people's lives, especially in G20 countries. Thus, this study is written to investigate the impact of the COVID-19 pandemic on stock market performance in G20 countries. This study uses daily stock market data of G20 countries from January 1, 2019 to June 30, 2020. The stock market data were divided into G7 countries and non-G7 countries. The data were analyzed using Long Short-Term Memory with a Recurrent Neural Network (LSTM-RNN) approach. The result indicated a gap between the actual stock market index and a forecasted time series that would have happened without COVID-19. Owing to movement restrictions, this study found that stock markets in six countries, including Argentina, China, South Africa, Turkey, Saudi Arabia, and the United States, are affected negatively. Besides that, movement restrictions in the G7 countries, excluding the United States, and the non-G20 countries, excluding Argentina, China, South Africa, Turkey, and Saudi, significantly impact the stock market performance. Generally, LSTM prediction estimates relative terms, except for stock market performance in the United Kingdom, the Republic of Korea, South Africa, and Spain. The stock market performance in the United Kingdom and Spain countries has significantly reduced during and after the occurrence of COVID-19. It indicates that the COVID-19 pandemic considerably influenced the stock markets of 14 G20 countries, whereas less severely impacting 6 remaining countries. In conclusion, our empirical evidence showed that the pandemic had restricted effects on the stock market performance in G20 countries.
Stochastic computing (SC) has a substantial amount of study on application-specific integrated circuit (ASIC) design for artificial intelligence (AI) edge computing, especially the convolutional neural network (CNN) algorithm. However, SC has little to no optimization on field-programmable gate array (FPGA). Scaling up the ASIC logic without FPGA-oriented designs is inefficient, while aggregating thousands of bitstreams is still challenging in the conventional SC. This research has reinvented several FPGA-efficient 8-bit SC CNN computing architectures, i.e., SC multiplexer multiply-accumulate, multiply-accumulate function generator, and binary rectified linear unit, and successfully scaled and implemented a fully parallel CNN model on Kintex7 FPGA. The proposed SC hardware only compromises 0.14% accuracy compared to binary computing on the handwriting Modified National Institute of Standards and Technology classification task and achieved at least 99.72% energy saving per image feedforward and 31× more data throughput than modern hardware. Unique to SC, early decision termination pushed the performance baseline exponentially with minimum accuracy loss, making SC CNN extremely lucrative for AI edge computing but limited to classification tasks. The SC's inherent noise heavily penalizes CNN regression performance, rendering SC unsuitable for regression tasks.
Since the launch of the InvestSmart™ initiative in 2014, the government agencies in Malaysia have been actively engaging community and university students via their outreach programs to promote investment literacy. Given this background, the state of the investment literacy of Malaysian undergraduates and their readiness to invest is intriguing. Therefore, this article offers a dataset of Malaysian undergraduates' readiness to invest and the role that investment literacy and social influence play in their readiness to invest. Using a non-probability sampling technique, 500 undergraduate students in Malaysia were engaged to participate voluntarily in this survey. Descriptive statistics are presented in this paper. The dataset provides insights into the current state of investment literacy among Malaysian undergraduates, the sources of information on stock investment, and the readiness of these undergraduates to participate in the stock market.