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This study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three‐step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits and rationalization of facility utilization is developed: (i) EV‐expanded network, (ii) multiagent‐based dynamic traffic assignment (MA‐DTA), and (iii) deployment refinement. Embedding the MA‐DTA and customized refinement strategy into the iterative planning structure enables the integration of operations and planning of the EFCS network. A numerical experiment and an empirical study in the Shandong Peninsula urban agglomeration are conducted. It demonstrates that the method can find a high‐quality solution within acceptable computation time and is applicable to realistic large‐scale EFCS planning. The planning method can effectively play the role of economy and facility in inducing EV users' charging demands, thus enhancing the overall ESER benefits. The integration of operation and planning is proven effective in reasonably matching the supply and demand of facility service and charging loads in a full‐time period. Further, the multistage EFCS planning schemes during 2025–2045 are explored, and some insightful policy implications are revealed. [ABSTRACT FROM AUTHOR]

This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers' electric vehicles (parked EVs) at low-battery levels. From the platform operator's perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model's validity and the solution method's efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms. Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126. [ABSTRACT FROM AUTHOR]

Developing a self‐sufficient renewable power (RP) road transport (SRPRT) system is an important future direction for transport–energy integration. More well‐developed studies must be conducted on the coordinated planning of transport, power supply, and power generation networks. This paper carries out the joint operation and planning of highway charging networks with the wind‐photovoltaic‐energy storage (HCN‐WPE) system. Under multi‐network integration and the interaction among multiple entities, a nested bi‐level optimization model is proposed to optimize the users' charging and travel behavior, charging network's deployment, and power generation system's (PGS) configuration. An H‐M‐L algorithm structure is developed, combining the heuristic algorithm, multi‐agent‐based simulation technology, and linear programming algorithm. Its convergence and applicability are verified on the Nguyen‐Dupius network. An empirical case in the Hu‐Bao‐Wu city agglomeration in China is employed to explore and discuss the managerial insights for the HCN‐WPE system. The study finds that multi‐network coordinated planning can improve the benefits of multiple entities, where the net present value, RP supply rate, and RP consumption rate increase by 12.0%, 3.2%, and 10.5%, compared to independent planning. Network‐level planning can play a management and induction role in balancing the station's load pressure. In addition, the PGS co‐configuration can leverage the complementary power supply of multiple RP generators and the peak cutting and valley filling of energy storage systems, which is essential for achieving the SRPRT goal. [ABSTRACT FROM AUTHOR]

Our case highlights an atypical presentation of Coxiella burnetii endocarditis with acute ischaemic stroke in a male patient in his 60s. The patient had undergone a congenital bicuspid aortic valve replacement with a bioprosthetic valve and an ascending aortic root graft with a pacemaker. He experienced intermittent fevers, chills and malaise over 15 months leading up to his presentation. During our diagnostic process, we used the newly developed Karius test, which detects microbial cell-free DNA in the patient’s serum. The patient was treated with doxycycline and hydroxychloroquine for 24 months along with surgical removal and replacement of his aortic valve and graft. Our case underscores the utility of the Karius test and the essential role of neurology in the multidisciplinary team. [ABSTRACT FROM AUTHOR]

Background: In China, disparities in the quality of stroke care still exist and implementing quality improvement is still a challenge. Aim: The aim of the study was to determine whether the intervention by Shanghai Stroke Service System (4S) has helped improve adherence to stroke care guidelines and patient outcome. Methods: The 4S is a regional stroke network with real-time data extraction among its 61 stroke centers in Shanghai. A total of 11 key performance indicators (KPIs) were evaluated. The primary outcomes were a composite measure and an all-or-none measure of adherence to 11 KPIs. The secondary outcomes were length of hospital stay and in-hospital mortality. Results: The study enrolled 92,395 patients (mean age 69.0 ± 12.5 years, 65.2% men) with acute ischemic stroke hospitalized within 7 days of onset in Shanghai from January 2015 to December 2020. More patients received guideline recommended care between 2018 and 2020 than those between 2015 and 2017 (composite measure 87.1% vs 83.6%; absolute difference 2.9%, 95% confidence interval (CI) = [2.7%, 3.2%], p < 0.001; all-or-none measure 49.2% vs 44.8% patients; absolute difference 3.5%, 95% CI = [2.7%, 4.2%], p < 0.001). Further analysis of individual KPIs showed an absolute increase in six KPIs ranging from 3.4% to 8.9% (p < 0.001 for all comparisons). Compared with 2015–2017, hospital length of stay was shorter (10.95 vs 11.90 days; absolute difference –1.08, 95% CI = [–1.18, –0.99], p < 0.001), and in-hospital mortality was significantly reduced (risk ratio (RR) = 0.88, 95% CI = [0.79, 0.98], p = 0.01) in 2018–2020. Conclusion: The 4S intervention was associated with increased adherence to the stroke care guidelines, which further translated to improved clinical outcomes. Trial registration: ClinicalTrials.gov identifier: NCT02735226. [ABSTRACT FROM AUTHOR]