ABSTRACT
During the current COVID-19 pandemic, older people are encouraged to use information, communication and technology in their everyday lives and discouraged from physical interactions. In Malaysia, it shows that implementing the Movement Control Order (MCO) to control the spread of the COVID-19 pandemic saw a shift in consumer trends, prompting businesses to explore new strategies to interact with consumers. But there is no study indicating the contributions of older people in this new business environment. Several complaints were reported regarding the usability, acceptability, and suitability of these applications for the elderly. The objectives of this study: (1) to investigate issues and challenges on older citizens in current software design quality;(2) To identify current practises on software usage amongst the older citizens, especially in e-commerce and e-business applications;and (3) To propose a model a software design quality that meets older people requirements, limitations, and expectations. The new model helps the developers design and construct software compatible with the elderly based on a quality perspective. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
This paper investigated the influence and interactions of air pollution concentrations by using the stochastic boosted regression trees between variables for each station and the impact of the COVID-19 Movement Control Order at Ipoh City air quality station. The one-hour data were gathered from the Department of Environment from January until June 2019 and 2020. Two thousand two hundred thirty-one data of particles, gases (Nitrogen oxides, Sulphur Dioxide, Ozone, Carbon Monoxide) concentrations and meteorological data (wind speed, wind directions, temperature, and relative humidity) were captured. The BRT model development process with an algorithm using a comprehensive package, R Software and its packages to understand the variability and trends. It was found that the relationship between the number of samples and number of trees (nt) of 4372 for oob were found the best iterations obtained. The performance of the boosting model was assessed and found that the FAC2 was 0.91, the R2 values were above 0.56 (R = 0.74), and the Index of Agreements (IOA) was 0.67, which fall ranges are within an acceptable for model performance. The Relative Variable Importance (RVI) that influenced PM2.5 for non-MCO data was CO (18.9% ), SO2 (14.6 %), O3 (12.9 %), and wd (10.66 %) while CO (22.6%), RH (13.4%), 14.7% and O3 (12.1%) were RVI factors influenced to PM2.5 concentrations during MCO periods. Estimating the strength of interaction effects (SIE) between variables was 0.24 for CO-wind directions, followed by 0.19 for ozone-wind speeds and 0.15 for NO2-CO. Results showed that the model developed was within the acceptable range and could be used to understand particles and identify important parameters that influence particle concentrations.