Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption

ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles

Automatically generating textual content with desired attributes is an ambitious task that people have pursued long. Existing works have made a series of progress in incorporating unimodal controls into language models (LMs), whereas how to generate controllable sentences with multimodal signals and high efficiency remains an open question. To tackle the puzzle, we propose a new paradigm of zero-shot controllable text generation with multimodal signals (\textsc{ZeroGen}). Specifically, \textsc{ZeroGen} leverages controls of text and image successively from token-level to sentence-level and maps them into a unified probability space at decoding, which customizes the LM outputs by weighted addition without extra training. To achieve better inter-modal trade-offs, we further introduce an effective dynamic weighting mechanism to regulate all control weights. Moreover, we conduct substantial experiments to probe the relationship of being in-depth or in-width between signals from distinct modalities. Encouraging empirical results on three downstream tasks show that \textsc{ZeroGen} not only outperforms its counterparts on captioning tasks by a large margin but also shows great potential in multimodal news generation with a higher degree of control. Our code will be released at https://github.com/ImKeTT/ZeroGen.Comment: 17 pages, preprin

Omega-3 Polyunsaturated Fatty Acids Antagonize Macrophage Inflammation via Activation of AMPK/SIRT1 Pathway

Macrophages play a key role in obesity-induced inflammation. Omega-3 polyunsaturated fatty acids (v-3 PUFAs) eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) exert anti-inflammatory functions in both humans and animal models, but the exact cellular signals mediating the beneficial effects are not completely understood. We previously found that two nutrient sensors AMP-activated protein kinase (AMPK) and SIRT1 interact to regulate macrophage inflammation. Here we aim to determine whether v-3 PUFAs antagonize macrophage inflammation via activation of AMPK/SIRT1 pathway. Treatment of v-3 PUFAs suppresses lipopolysaccharide (LPS)-induced cytokine expression in macrophages. Luciferase reporter assays, electrophoretic mobility shift assays (EMSA) and Chromatin immunoprecipitation (ChIP) assays show that treatment of macrophages with v-3 PUFAs significantly inhibits LPS-induced NF-kB signaling. Interestingly, DHA also increases expression, phosphorylation and activity of the major isoform a1AMPK, which further leads to SIRT1 overexpression. More importantly, DHA mimics the effect of SIRT1 on deacetylation of the NF-kB subunit p65, and the ability of DHA to deacetylate p65 and inhibit its signaling and downstream cytokine expression require SIRT1. In conclusion, v-3 PUFAs negatively regulate macrophage inflammation by deacetylating NF-kB, which acts through activation of AMPK/SIRT1 pathway. Our study defines AMPK/SIRT1 as a novel cellular mediator for the anti-inflammatory effects of v-3 PUFAs

Development of Model-based Transit Signal Priority Control for local Arterials

AbstractThis paper presents a transit signal priority (TSP) model designed to benefit both bus riders and passenger-car users. Most of conventional priority methods are applied at the isolated intersection. However, this kind of control strategies may failed to reduce the travel time since the prioritized buses have to stop at the downstream intersections. Therefore, along the line of headway-based research, this study intends to develop a new TSP control approach with the concerns of bus passenger delay on the entire arterial. Moreover, a basic method for queue length estimation is presented to evaluate the impacts of TSP control on passenger cars. The control objective is to minimize bus passenger waiting time at the downstream bus stop, simultaneously ensuring the total person delay of entire intersection is not increased. Using the microscopic simulation, the proposed strategy has shown its benefits in reducing bus passenger waiting time and total intersection delay