In order to meet the market demands for customization and rapid response while controlling carbon emissions in manufacturing systems, it has become increasingly important to consider the collaborative optimization of equipment layout in smart production units alongside intelligent storage and transportation systems. This study investigates the loop layout problem of smart manufacturing units equipped with Automated Guided Vehicles (AGVs) through a novel simulation-optimization framework integrating intelligent search algorithms with directed graph-based isomorphism detection. An optimization model is presented with the objective functions of maximizing production capacity and minimizing energy consumption, constrained by performance indicators such as relative position of the equipment within the unit, AGV transport speeds and buffer capacity. Given the lack of a closed mathematical expression for this multi-objective function, a second-generation NSGA-II based on simulation is designed to solve the model. Furthermore, a method based on directed graph isomorphic layout processing is proposed to quickly eliminate similar solutions, enhancing solution quality and algorithm efficiency. Through comparative experimental design and practical applications in intelligent workshops, the effectiveness, superiority, and application value of this optimization algorithm in addressing the circular layout problem of smart production units are validated.

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.

Energy-saving design is becoming a trending topic and top-priority over the past decades due to high energy costs, limited available resources and growing urban development. Buildings are alluded to as the major contributors of energy consumption and environmental emissions across the globe. This calls for the development of precise forecasting models of energy consumption and carbon emissions. Hence, this research paper harnesses the implementation of several contemporary metaheuristics to accurately project heating and cooling energy (HEN and CEN) in residential buildings. In this respect, black widow optimization, dandelion optimization, dingo optimization, sparrow search, and red-tailed hawk optimization are among the studied metaheuristics in this research study. The prediction accuracies of the developed models are assessed stepping on the measures of i) relative absolute error (RAE), ii) mean absolute error (MAE), iii) mean absolute percentage error (MAPE), iv) root mean squared error (RMSE) and v) Nash-Sutcliffe efficiency (NSE). It is shown that the developed red-tailed hawk optimization-based model succeeded in accomplishing the most precise results of HEN and CEN. In this context, it predicted HEN with RAE (0.201), MAE (1.838), MAPE (7.626%), RMSE (2.826), and NSE (0.921). Besides, it anticipated CEN with RAE (0.234), MAE (2.009), MAPE (7.519%), RMSE (3.246), and NSE (0.883). It can be argued that this research study could benefit architects and designers in creating more energy-efficient buildings at an early stage.

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.