ABSTRACT
With the recent global increase in fossil energy prices post Covid-19 and the drive to enhance sustainability towards NetZero, renewable energy is becoming one of the key global technologies to power societies at an affordable cost. This paper presents a novel study in relation to solar energy use in residential dwellings in Jordan, to discuss the benefits and challenges of using domestic solar energy systems within the current context of increasing energy prices. The Self-Determination Theory and Maslow's Hierarchical Theory are discussed in-line with the findings. This study, in addition to literature review, has utilised qualitative and quantitative data collected from an on-line survey with 366 participants to investigate Jordanian consumers' energy consumption behaviour, perception of renewables, and major factors influencing solar energy adoption. The novelty of this study that it provides a bench mark of affordability for future initiatives. The Jordan Renewable Energy & Energy Efficiency Fund is currently creating several initiatives to drive the society to adopt renewable energy. The results of this study will help to identify the crucial factors that could be hindering the adoption and expansion of renewables, particularly solar energy. This work has investigated awareness, motivation, difficulties, affordability and level of satisfaction in relation to solar energy in domestic dwellings. The results of this study have shown that Jordanians believe that financial affordability and awareness are both crucial for utilising renewables. For current users of solar systems, there is an increased satisfaction in their performance levels. However, energy storage is critical for enhancing the implementation of solar energy due to the complexity of grid-connected systems and the need for off-grid installations. Therefore, if renewable energy providers and governmental bodies aim to expand the implementation of solar energy technology and enhance public engagement, then it can be suggested that they should expand the promotion process of solar energy through platforms and further initiatives that engage with the public and subsidise the cost to provide more affordable solar energy systems for residential dwellings. The aim is to decrease carbon emission, reduce energy cost and enhance sustainability towards Net Zero Carbon emission.
ABSTRACT
The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being-in some cases-the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users' ages and the duration of the SM usage (H.mean) were also considered. The decision tree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning.