Charging facility allocation in smart cities

The raising concerns of energy consumption and air pollution advance the development of electric vehicle technologies and promote the increased deployment of Electric Vehicles (EVs) towards electric transportation. The increasing number of EVs on the road network leads to a growing challenge of electricity management for the power grid to promptly supply electricity to EVs. In order to address this challenge, we need to carefully plan the energy sources and energy delivery via charging facilities to EVs, taking into consideration interdependencies between roads/transportation and electric grid. In this thesis, we focus on studying the placement of energy sources and their charging facilities for EVs by developing: 1) an extended Flow Refueling Location model which finds optimal locations for charging stations as well as dynamic wireless charging pads, and 2) a 2-stage planning process for placement of charging station. The first stage of the planning process is to determine the optimal locations for placing the charging stations to serve the maximum amount of EVs on the road network. Given the selected optimal locations, the second stage determines the capacity of the charging service locations with the purpose of minimizing the total waiting time of EV drivers across the road network to charge their EVs. We show the effectiveness of these two planning models on a sample road network during our performance evaluation

A multicentre, prospective, double-blind study comparing the accuracy of autoantibody diagnostic assays in myasthenia gravis: the SCREAM studyResearch in context

Summary: Background: Laboratory determination of autoantibodies against acetylcholine receptor (AChR), muscle-specific kinase (MuSK) and other autoantigens have been integrated into the diagnosis of myasthenia gravis (MG). However, evidence supporting the selection of methodologies is lacking. Methods: In this prospective, multicentre cohort study, we recruited patients with suspected MG to evaluate the diagnostic accuracy of cell-based assay (CBA), radioimmunoprecipitation assay (RIPA) and enzyme-linked immunosorbent assay (ELISA) in detecting AChR and MuSK autoantibodies. This study is registered with www.clinicaltrials.gov, number NCT05219097. Findings: 2272 eligible participants were recruited, including 2043 MG, 229 non-MG subjects. AChR antibodies were detected in 1478, 1310, and 1280 out of a total of 2043 MG patients by CBA, RIPA, and ELISA, respectively; sensitivity, 72.3% (95% CI, 70.3–74.3), 64.1% (95% CI, 62.0–66.2), 62.7% (95% CI, 60.5–64.8); specificity, 97.8% (95% CI, 95.0–99.3), 97.8% (95% CI, 95.0–99.3), 94.8% (95% CI, 91.9–97.7). MuSK antibodies were found in 59, 50, and 54 from 2043 MG patients by CBA, RIPA and ELISA, respectively; sensitivity, 2.9% (95% CI, 2.2–3.7), 2.4% (95% CI, 1.8–3.2), 2.6% (95% CI, 2.0–3.4); specificity, 100% (95% CI, 98.4–100), 100% (95% CI, 98.4–100), and 99.1% (95% CI, 96.9–99.9). The area under the curve of AChR antibodies tested by CBA was 0.858, and there were statistical differences with RIPA (0.843; p = 0.03) and ELISA (0.809; p < 0.0001). Interpretation: CBA has a higher diagnostic accuracy compared to RIPA or ELISA in detecting AChR and MuSK autoantibodies for MG diagnosis. Funding: New Terrain Biotechnology, Inc., Tianjin, China