During the last decades, environmental crises through energy consumption and economic growth were noticed as a growing concern among researchers. The industrial sector is the main part of economic growth in each country using conservative energy and emitting carbon dioxide that causes global warming. The Paris agreement and Kyoto protocol were two agreements to prevent governments from emitting CO2 freely. Purpose of this research was to investigate the cement industry, steel industry, and automobile industry products’ effects on CO2 emissions in Iran and to rank them according to their measured effects on CO2 emissions. The methodology used in this study was to estimate equations with CO2 emissions as a dependent variable and cement, steel, and automobile industries’ products with ordinary least squares (OLS) and generalized moment method (GMM) approaches. Stationary, Johansen cointegration, Durbin-Watson, Breusch-Godfrey, Chow breakpoint, and normal residual tests were checked. In-sample forecasting was implemented to check the precision of the estimation and an updated ranking was reported as a final result to consider which industry has affected CO2 emissions more than the others per unit of production cost. In conclusion, the cement industry, steel industry, and automobile industry had the most positive effects on CO2 emissions, respectively. This result is suitable to prioritize the industries for enhancing green technology and optimizing industrial production for a more sustainable economy.

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The trend toward sustainable city development is associated with intelligent transportation systems (ITS). Automation, efficiency, safety, security, and cost-effectiveness are critical factors in establishing each aspect of a smart city. Real-time data obtained from ITS play an essential role in improving the level of service of road segments, enhancing road safety, and supporting road users with road circumstances information. Travel time information is applicable in travel time maps, decision makings for traffic congestion, dynamic pricing of the network, emergency relief services, traffic flow monitoring, traffic jams management, and air quality analysis. Travel time on a road segment highly depends on geometrical specifications, environmental and weather conditions, traffic flow, and driving behavior. Due to specific driving behavior and road conditions, the above parameters are not essentially applicable in another region. The present research uses the data collected from loop detectors and License Plate Recognition (LPR) systems to develop a Bureau of Public Roads (BPR) model for Iran’s freeway network (Tehran-Qom Freeway). Because of the large amount of data, the SQL server program was used for creating and organizing the database and the BPR model was calibrated using SPSS statistical software. The results of the BPR model were evaluated with an ANOVA test, indicating that the derived model can estimate the travel time at freeway sections with a %5.2 error for the volume-to-capacity ratio (V/C) of less than 0.8.

Today, there are increasing demands for flying drones with diverse capabilities for civilian and military uses, and there is growing attention given to this topic. When it comes to drone operations, the amount of energy they consume is a determining factor in their ability to achieve their full potential. According to the nature of the problem, it appears that it is necessary to identify the factors affecting the energy consumption of UAVs during the execution of missions as well as examine the general factors that influence the consumption of energy. The purpose of this chapter is to provide an overview of the current state of research in the area of UAV energy consumption. This is followed by general categorizations of factors affecting UAV's energy consumption as well as an investigation of different energy models.

In general, customers are looking to receive their orders in the fastest time possible and to make purchases at a reasonable price. Consequently, the importance of having an optimal delivery time is increasingly evident these days. One of the structures that can meet the demand for large supply chains with numerous orders is the hierarchical integrated hub structure. Such a structure improves efficiency and reduces chain costs. To make logistics more cost-effective, hub-and-spoke networks are necessary as a means to achieve economies of scale. Many hub network design models only consider hub type but do not take into account the hub scale measured using freight volume. This paper proposes a multi-objective scheduling model for hierarchical hub structures (HHS), which is layered from top to bottom. In the third layer, the central hub takes factory products from decentralized hubs and sends them to other decentralized hubs to which customers are connected. In the second layer, non-central hubs are responsible for receiving products from the factory and transferring them to central hubs. These hubs are also responsible for receiving products from central hubs and sending them to customers. Lastly, the first layer contains factories responsible for producing products and providing for their customers. The factory uses the flexible flow-shop platform and structure to produce its products. The model’s objective is to minimize transportation and production costs as well as product arrival times. To validate and evaluate the model, small instances have been solved and analyzed in detail with the weighted sum and ε-constraint method. Consequently, based on the mean ideal distance (MID) metric, two methods were compared for the designed instances.