Design and Fabrication of a Mutual Control Electronic Circuit for Solar and Electrical Water Heating

This research is a temperature controller that will be implemented to ensure that the water temperature of the solar water heating unit is maintained at the desirable level at all times of use. This control circuit is designed to control the On/Off action of the immersed electrical heater according to specific temperature range. A temperature sensor will sense the water temperature constantly and send signal to a micro-controller unit. The micro-controller will process the data according to a written program and control the actions of electrical heater. At the same time, temperature reading will be displayed through LCD and real-time data can be viewed from a computer via serial port. During times of sufficient sunlight, solar energy will be the main source used for heating water; otherwise, there will be an automatic switching to the electrical operated immersion heater. This controller will give reliability to users of solar water heating systems

Analysis of stock market reaction in Malaysia during Covid-19 pandemic via ARIMA

Investment has become a major money-making business in this world through investment in financial markets, stock markets, and forex. There is uncertainty about which stocks to buy during a pandemic like Covid-19. Poor investment choices will affect the profitability of the investors, shifting their risk appetite to be more defensive. Thus, this research is carried out to study the movement of stocks for the year 2020 in Malaysia based on the data obtained from Yahoo Finance. The objective is to provide investors with a guide to investing efficiently during a pandemic. This study investigates how Covid-19 impacts the rate of growth or reduction of stocks so that the performance of the stocks can be forecasted in the future. The moving average method is used to analyze the trend of the stock by comparing the top gainers against the top losers during the periods of pre Covid-19 and Covid-19. The Autoregressive Integrated Moving Average (ARIMA) model studies the autocorrelation function (ACF) graph of selected stocks to further understand the movement of the stocks and compare it to the closing prices of the selected stocks. Based on the findings, it was demonstrated that the quantitative method used could be used to study the effects of a pandemic, as well as the severity of the losses incurred, and profits earned by the industries. The industries that are essential to the country, like pharmaceuticals and rubber manufacturing, are able to maintain their businesses. Moreover, these sectors have profited from the pandemic. The tourism and aviation industries have been hit the hardest by the pandemic, as evidenced by falling stock prices. Thus, it would be wise for investors to invest in an essential sector company during the Covid-19 pandemic

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

Gender recognition is trivial for a physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) (Hsu et al.) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SVM). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient and effective for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Barani’s work (90.34%), Nazarloo’s work (92.50%), and other classifiers

Investigation of multiparameter trends and anthropometric measurements for cardiorespiratory fitness assessment among UTM staff

Cardiorespiratory fitness (CRF) is known to reduce metabolic-related diseases like cardiovascular diseases (CVD), obesity, hypertension, and type II diabetes. On the other hand, the gold standard to measure CRF is by measuring maximal oxygen consumption, VO2 max over the years. This study is performed to identify parameters that influence CRF without solely relying on invasive features such as VO2 max. A number of 31 UTM staff aged between 30 and 40 years old have participated in this study with 17 female subjects and 14 male subjects. Anthropometric measurements are obtained by direct measurement and body composition analysis using a body composition monitor. Multiparameter trend measurements were obtained from vital sign monitors at rest. Single feature analysis was performed in terms of accuracy, specificity and sensitivity to identify which feature influences CRF the most. The features collected are body mass index (BMI), body fat (BF), muscle mass (MM), bone density (BD), waist circumference (WC), resting heart rate (RHR), resting systolic blood pressure (RSBP), forced expiratory volume in one second (FEV1), and recovery trend heart rate (RecHR). Next, all these features were validated using Naïve Bayes (NB) and Decision Tree (DT) classifiers. Finally, six features which are BF, BM, BD, RHR, RSBP and FEV1, with accuracy more than 70% were selected and identified as the features which influence CRF of UTM staff