(M = 4.35 ± 0.54) compared to Speech Science (M = 3.75 ± 0.59) in professional identity subscale whereas in students preparation subscale for interprofession education, only Physiotherapy students had higher score (M = 4.15 ± 0.82) in comparison to Diagnostic Imaging & Radiotherapy (M = 3.25 ± 0.83). Besides, independent T test showed the students were in favour of having IPL in early year of education with the average score (M = 3.53 ± 1.029) for year one and year two compared to year three to fi ve (M = 3.34 ± 1.089). The result form this research shows that the students have a positive perception towards IPL based on the average values not more than 3.0.
Electricity price forecasting (EPF) is important for energy system operations and management which include strategic bidding, generation scheduling, optimum storage reserves scheduling and systems analysis. Moreover, accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Nevertheless, accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity price. This work proposes a mid-term forecasting model based on the demand and price data, renewable and non-renewable energy supplies, the seasonality and peak and off-peak hours of working and nonworking days. An optimized Gated Recurrent Unit (GRU) which incorporates Bagged Regression Tree (BTE) is developed in the Recurrent Neural Network (RNN) architecture for the mid-term EPF. Tanh layer is employed to optimize the hyperparameters of the heterogeneous GRU with the aim to improve the model's performance, error reduction and predict the spikes. In this work, the proposed framework is assessed using electricity market data of five major economical states in Australia by using electricity market data from August 2020 to May 2021. The results showed significant improvement when adopting the proposed prediction framework compared to previous works in forecasting the electricity price.
Cryptocurrency symbolizes of a new development in the financial sector since it is the world's first entirely decentralized digital payment system. The cryptocurrency known as virtual money is one of the most important innovations brought on by digitalization. The purpose of this study is to analyze the relationship between the cryptocurrency (Bitcoin, Monero, and Stellar) with macroeconomics variables known as stock price index (Dow Jones dan Nikkei), oil price (Brent Oil dan WTI), and exchange rates (Australian Dollar, Euro, and Pound Sterling). The data was obtained from investing.com on monthly basis for the period between January 2016 untuil December 2020. The analysis were conducted based on unit root test, co-integration and vector error correction model (VECM) in order to identify the relationship between the three selected cryptocurrencis with macroeconomic variables. The findings of this paper showed that there is cointegration between the variables. The Vector Error Correction Model (VECM) indicates that the Bitcoin model and Stellar model did not have a long-run relationship. While for the second model, Monero found to have a long-run relationship with the variables. This research contributes to the growing study on cryptocurrency while extend and complement the literature by sourcing the latest research paper on this related field.
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