Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Preparation and Free Radical Detection of Doped ZnO Nanomaterials

Nanomaterials play an important role in the field of optics and medicine, and rare earth cerium can act as a catalyst and a dopant to increase improve its optical properties. Used nanometer zinc oxide as a carrier, the Ag-ZnO and Ag-Ce-ZnO samples were prepared with the sol-gel method and characterized by X-ray diffractometer. The nanometer zinc oxide materials can generate the free hydroxyl radicals, so the methylene blue solution (MB) was used as the capture agent, and the free radicals produced by the doped nanometer zinc oxide were detected by UV-spectrophotometry. The results showed that the preparation particle size of the Ag-ZnO sample was about 75nm, particle size of the Ag-Ce-ZnO sample was about 70nm. The concentration of two samples treated with the dispersant PAAS generated hydroxyl radicals is much larger than the two samples treated with the dispersant SDBS. The doping of Ce ions not only improved the optical activity of the samples, but also increased the content of hydroxyl radicals. Rare-earth doped effe ctively improved the optical properties of the ZnO nanoparticles, and the effect of sample Ag-Ce-ZnO was better than Ag-ZnO samples. Discussion and study on the nature of both the nanometer zinc oxide and doped nanometer zinc oxide, as well as the method of preparation and the theory of produce hydroxyl radicals. In the experiment, doped nano-ZnO was used as carrier and methylene blue (MB) as capture reagent. This study provided a new method for the detection of free radicals drugs

Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand HDV behaviors to make safe actions. In this study, we develop a Driver Digital Twin (DDT) for the online prediction of personalized lane change behavior, allowing CAVs to predict surrounding vehicles' behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge-cloud architecture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the lane change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles connected through the 4G/LTE cellular network. The lane change intention can be recognized in 6 seconds on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 meters within a 4-second prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM

Effects of clinical medications on male fertility and prospects for stem cell therapy

An increasing number of men require long-term drug therapy for various diseases. However, the effects of long-term drug therapy on male fertility are often not well evaluated in clinical practice. Meanwhile, the development of stem cell therapy and exosomes treatment methods may provide a new sight on treating male infertility. This article reviews the influence and mechanism of small molecule medications on male fertility, as well as progress of stem cell and exosomes therapy for male infertility with the purpose on providing suggestions (recommendations) for evaluating the effect of drugs on male fertility (both positive and negative effect on male fertility) in clinical application and providing strategies for diagnosis and treatment of male infertility

A Facile and Effective Method to Fabricate Superhydrophobic/Superoeophilic Surface for the Separation of Both Water/Oil Mixtures and Water-in-Oil Emulsions

Superhydrophobic/superoleophilic surfaces (water contact angle greater than 150° with low hysteresis, with an oil contact angle smaller than 5°) have a wide-range of applications in oil/water separation. However, most of the essential methods to fabricate this kind of surface are complex, inflexible, and costly. Moreover, most methods focus on separating immiscible oil and water mixtures but lack the ability to demulsify surfactant-stabilized emulsions, which is widely present in industry and daily life. In this study, a facile and effective method was developed to fabricate superhydrophobic/superoleophilic surfaces that can be easily applied on almost all kinds of solid substrates. The treated porous substrates (e.g., steel mesh; cotton) can separate oil/water mixtures or absorb oil from a mixture. Furthermore, the compressed treated cotton is capable of demulsifying stabilized water-in-oil emulsions with high efficiency. The simple, low-cost, and material-unrestricted method provides an efficient way to separate oil/water mixtures of various kinds and has great potential in energy conservation and environmental protection