The hybridization of natural and synthetic fibers leads to composites’ optimum mechanical properties. In this study, an attempt was made to study the effect of the stacking sequence on PBS-based Glass-Jute (GJ) hybrid composites. Six types of hybrid composite, each containing five different layers of jute and glass fabric, were manufactured by the compression molding method. Mechanical properties, such as tensile, flexural, and impact resistance were studied and analyzed in detail. The surface characterization of the composites was performed through scanning electron microscopic images. The moisture absorption properties were also investigated by immersing the composites in distilled water for one week at ambient temperature. The TGA test was conducted to study their thermal properties. The experimental results showed that the stacking sequence of the fiber layers has a significant effect on the overall performance of GJ hybrid composites. Among the hybrid GJ composites, composites with glass fiber layers on their outer surfaces showed optimum mechanical, thermal, and water resistance properties.

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Every metropolitan trip is punctuated by traffic signals, which have an immediate effect on drivers, the environment, and the economy whether the route is crowded or not. Traffic signal automation to reduce traffic delay is a major issue all over the world. Nevertheless, the current solutions to reduce exponentially rising traffic issues are not completely dealing with the problem. Companies, traffic engineers and researchers have suggested several Traffic Signal control systems. The main function of the traffic signal management system is to coordinate individual traffic signals to accomplish operational goals for the entire network. The single junction-based systems are unable to reduce the waiting time of exponentially increasing traffic load on the roads. To deal with this, we propose collaborative signal automation on a traffic simulator based on reinforcement learning techniques. The model utilized a q-learning technique that depicts composing units of addressed issues: agents, surrounding and response. The collaborative network takes advantage of traffic flow prediction with signal automation. Multi-junction road environments and vehicles are fed to the network as input. The proposed system suggests optimal signal automation to alleviate delay time and sequence length of traffic. Q-learning-based model decreases the wait time and leads to a steady flow of vehicles with several significances in composite traffic areas.