14 research outputs found

    A spatio-temporal deep learning model for short-term bike-sharing demand prediction

    Get PDF
    Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems

    Evaluation of prophylactic dosages of Enoxaparin in non-surgical elderly patients with renal impairment

    Get PDF
    BACKGROUND: Thromboprophylaxis dosing strategies using enoxaparin in elderly patients with renal disease are limited, while dose adjustments or monitoring of anti-Xa levels are recommended. We sought to evaluate the efficacy and safety of enoxaparin 20 mg versus 30 mg subcutaneously daily by comparing anti-Xa levels, thrombosis and bleeding. METHODS: We conducted a prospective, single-blinded, single-center randomized clinical trial including non-surgical patients, 70 years of age or older, with renal disease requiring thromboprophylaxis. Patients were randomized to receive either 20 mg or 30 mg of enoxaparin. The primary endpoint was peak anti-Xa levels on day 3. Secondary endpoints included trough anti-Xa levels on day 3, achievement of within range prophylactic target peak anti-Xa levels and the occurrence of hemorrhage, thrombosis, thrombocytopenia or hyperkalemia during hospitalization. RESULTS: Thirty-two patients were recruited and sixteen patients were randomized to each arm. Mean peak anti-Xa level was significantly higher in 30 mg arm (n = 13) compared to the 20 mg arm (n = 11) 0.26 +/- 0.11, 95%CI (0.18-0.34), versus 0.14 +/- 0.09, 95CI (0.08-0.19) UI/ml, respectively; p = 0.004. Mean trough anti-Xa level was higher in 30 mg arm (n = 10) compared to the 20 mg arm (n = 16), 0.06 +/- 0.03, 95CI (0.04-0.08) versus 0.03 +/- 0.03, 95CI (0.01-0.05) UI/ml, respectively; p = 0.044. Bleeding events reported in the 30 mg arm were one retroperitoneal bleed requiring multiple transfusions, and in the 20 mg arm one hematuria. No thrombotic events were reported. CONCLUSION: Peak anti-Xa levels provided by enoxaparin 20 mg were lower than the desired range for thromboprophylaxis in comparison to enoxaparin 30 mg. TRIAL REGISTRATION: The trial was retrospectively registered on ClinicalTrials.gov identifier: NCT03158792 . Registered: May 18, 2017

    Unusual case of colonized pacemaker lead presenting with endocarditis, hemoptysis and tricuspid valve stenosis

    No full text
    The present report is the first to describe a case of hemoptysis caused by an endocardial pacemaker lead. In addition, the patient presented with endocarditis and tricuspid valve stenosis. Aggressive treatment consisted of surgical extraction of two pacemaker leads and one pacemaker battery, replacement of the tricuspid valve and implantation of a DDD-R epicardial pacemaker
    corecore