The increasing worldwide focus on sustainable development has drawn significant attention to environmental issues. The focus is on minimizing energy usage to lessen environmental harm. However, conversations regarding this subject have raised worries. Sustainable development is gaining respect from economists and policymakers due to its potential impact on productivity. The reason for conducting this research is to evaluate the effects of energy consumption, economic growth, trade liberalization, and urbanization on carbon emissions within the context of Saudi Arabia. The data was collected longitudinally from 1980 to 2022 and comes from World Development Indicators and Our World in Data. The study found cointegration among the variables using the ARDL model Short-term CO2 emissions were inversely related to previous delays, economic growth, and trade liberalization, but directly connected to energy consumption and urbanization. Long-term data demonstrates that the usage of energy, urbanization, and trade liberalization have a positive correlation with CO2 emissions, but economic development and previous CO2 emissions have a negative correlation. The study's results are briefly discussed, and several recommendations are made accordingly.

The present research analyzes the short and long run relationship between Energy Consumption, Economic growth, and Carbon Dioxide emissions in Jordan. The study employs two (2) models: 1: Autoregressive Distributed Lag (ARDL) bound testing approach and 2: Vector Error Correction Model (VECM) Granger causality and impulse response function. The results reveal that energy consumption has a positive impact on carbon dioxide emissions and in turn carbon dioxide emissions have a positive link to economic growth. Further, the Environmental Kuznets Curve (EKC) hypothesis is tested, and it reveals that the EKC hypothesis is validated in the case of Jordan since the carbon dioxide emissions show a significant impact on economic growth in the short and long run. The study provides important results for future researchers and government policy makers.

Economic growth is very basic need of any economy but its environmental effects should not be ignored. We investigate the environmental effects of economic growth and energy consumption of Saudi Arabia. The study uses data of a period 1968-2014 and cointegration test and corroborates a long- and short-run relationships. The results indicate that economic growth and energy consumption contributes in CO2 emissions in both long- and short-run. It means that increasing economic growth of the Kingdom has social cost on the economy in terms of pollution emissions. Based on findings, we recommend to use the alternative renewable sources of energy consumption to avoid the pollution effects of growth in Saudi Arabia.

The continuous increase in energy consumption has brought worldwide attention to its significant environmental effect, which is triggered by the increase in greenhouse gas emissions, global warming, and rapid climate change. As such, more energy efficient buildings are required to minimize the energy consumption of heating and cooling. The present study introduces a set of machine learning-based models to predict the heating and cooling loads in buildings. This includes back-propagation artificial neural network, generalized regression neural network, radial basis neural network, radial kernel support vector machines and ANOVA kernel support vector machines. The comparisons were conducted as per mean absolute percentage error (MAPE), mean absolute error (MAE) and root-mean squared error (RMSE). Finally, the significance of the capacities of the machine learning models are evaluated using two-tailed student’s t-tests. Results demonstrate that the radial basis function network outperformed the aforementioned machine learning models.

The use of elastic elements in actuators can be effective in improving their performance. The present paper examines the different layouts of the elastic element in actuators, choosing the appropriate design, and then designing a series elastic actuator for use in an active knee orthosis. For this purpose, the design elements of actuators were extracted using the knee data for walking. In the next step, considering the effect of the spring stiffness on the energy consumption and actuator’s peak power, its optimum amount for gait was calculated. Afterwards, the effect of adding the spring has been shown at an optimal value. Since the human knee in a gait cycle involves position and force control, designing an efficient controller to track the force and position of the knee during the gait cycle is the next section of this study. Due to the specific application of this actuator, its volume and size are important for the user. For this reason, it is necessary to design and construct an actuator with a suitable size and weight, with good output. For this purpose, a lightweight and low volume actuator was fabricated.

Mobile Ad hoc Network (MANET) consists of some nodes, which are randomly placed in operational environment. This type of network does not have any infrastructure and nodes are completely dynamic and moveable. Each node can contact with the other in-range nodes. One of the disadvantages of MANET is the low lifetime of nodes power in which the energy consumption of the network goes up due to connectivity overheads. In this paper, by presenting a Fuzzy clustering algorithm, we endeavor for a more optimized energy consumption of this network by reducing the number of re-election of clusterheads (CHs) and reducing the re-affiliation of nodes and also by selecting the best node as CH amongst current members of each cluster. The result of simulation shows that our algorithm performs better than other existing algorithm.