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A novel additive manufacturing process utilizing the laminated object manufacturing (LOM) technology with woven natural fiber-reinforced biopolymer is investigated in this paper. Traditional synthetic composite materials are products from nonrenewable crude oil with limited end-of-life options, and therefore not environmentally friendly. The continuous woven natural fiber is used to significantly strengthen the mechanical properties of biocomposites and PLA biopolymer as the matrix made the material completely biodegradable. This is one of the promising replacements for synthetic composites in applications such as automotive panels, constructive materials, and sports and musical instruments. A LOM 3D printer prototype has been designed and built by the team using a laser beam in cutting the woven natural fiber reinforcement and molten PLA powder to bind layers together. Tensile and flexural properties of the LOM 3D printed biocomposites were measured using ASTM test standards and then compared with corresponding values measured from pure PLA specimens 3D printed through FDM. Improved mechanical properties from LOM 3D-printed biocomposites were identified by the team. SEM imaging was performed to identify the polymer infusing and fiber-matrix binding situations. This research took advantage of both the material and process’s benefits and combine them into one sustainable practice. 

Polarization aberrations are found in most optical components due to a materials differing response to s- and p-polarizations. This differing response can manifest either as diattenuation, retardance, or both. Correction of polarization aberrations, such as these, are critical in many applications such as interferometry, polarimetry, display, and high contrast imaging, including astronomy. In this work, compensators based on liquid crystal polymer and anti-reflection thin-films are presented to correct polarization aberrations in both transmission and reflection configurations. Our method is versatile, allowing for good correction in transmission and reflection due to optical components possessing differing diattenuation and retardance dispersions. Through simulation and experimental validation we show two designs, one correcting the polarization aberrations of a dichroic spectral filter over a 170nm wavelength band, and the other correcting the polarization aberration of an aluminum-coated mirror over a 400nm wavelength band and a 55-degree cone of angles. The measured performance of the polarization aberration compensators shows good agreement with theory. 

Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using realworld vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically. 

This study focuses on how to improve the merge control prior to lane reduction points due to either accidents or constructions. A Cooperative Car-following and Merging (CCM) control strategy is proposed considering the coexistence of Automated Vehicles (AVs) and Human-4 Driven Vehicles (HDVs). CCM introduces a modified/generalized Cooperative Adaptive Cruise Control (CACC) for vehicle longitudinal control prior to lane reduction points. It also takes courtesy into account to ensure that AVs behave responsibly and ethically. CCM is evaluated using microscopic traffic simulation and compared with no control and CACC merge strategies. The results show that CCM consistently generates the lowest delays and highest throughputs approaching the theoretical capacity. Its safety benefits are also found to be significant based on vehicle trajectories and density maps. AVs in this study do not need to be fully automated and can be at Level-1 automation. CCM only requires automated longitudinal control such as Adaptive Cruise Control (ACC) and information sharing among vehicles, and ACC is already commercially available on many new vehicles. Also, it does not need 100% ACC penetration, presenting itself as a promising and practical solution for improving traffic operations in lane reduction transition areas such as highway work zones.