Sigh in patients with acute hypoxemic respiratory failure and acute respiratory distress syndrome: the PROTECTION pilot randomized clinical trial

Background: Sigh is a cyclic brief recruitment manoeuvre: previous physiological studies showed that its use could be an interesting addition to pressure support ventilation to improve lung elastance, decrease regional heterogeneity and increase release of surfactant. Research question: Is the clinical application of sigh during pressure support ventilation (PSV) feasible? Study design and methods: We conducted a multi-center non-inferiority randomized clinical trial on adult intubated patients with acute hypoxemic respiratory failure or acute respiratory distress syndrome undergoing PSV. Patients were randomized to the No Sigh group and treated by PSV alone, or to the Sigh group, treated by PSV plus sigh (increase of airway pressure to 30 cmH2Ofor 3 seconds once per minute) until day 28 or death or successful spontaneous breathing trial. The primary endpoint of the study was feasibility, assessed as non-inferiority (5% tolerance) in the proportion of patients failing assisted ventilation. Secondary outcomes included safety, physiological parameters in the first week from randomization, 28-day mortality and ventilator-free days. Results: Two-hundred fifty-eight patients (31% women; median age 65 [54-75] years) were enrolled. In the Sigh group, 23% of patients failed to remain on assisted ventilation vs. 30% in the No Sigh group (absolute difference -7%, 95%CI -18% to 4%; p=0.015 for non-inferiority). Adverse events occurred in 12% vs. 13% in Sigh vs. No Sigh (p=0.852). Oxygenation was improved while tidal volume, respiratory rate and corrected minute ventilation were lower over the first 7 days from randomization in Sigh vs. No Sigh. There was no significant difference in terms of mortality (16% vs. 21%, p=0.342) and ventilator-free days (22 [7-26] vs. 22 [3-25] days, p=0.300) for Sigh vs. No Sigh. Interpretation: Among hypoxemic intubated ICU patients, application of sigh was feasible and without increased risk

Robot Co-design: Beyond the Monotone Case

© 2019 IEEE. Recent advances in 3D printing and manufacturing of miniaturized robotic hardware and computing are paving the way to build inexpensive and disposable robots. This will have a large impact on several applications including scientific discovery (e.g., hurricane monitoring), search-and-rescue (e.g., operation in confined spaces), and entertainment (e.g., nano drones). The need for inexpensive and task-specific robots clashes with the current practice, where human experts are in charge of designing hardware and software aspects of the robotic platform. This makes the robot design process expensive and time consuming, and ultimately unsuitable for small-volumes low-cost applications. This paper considers the computational robot co-design problem, which aims to create an automatic algorithm that selects the best robotic modules (sensing, actuation, computing) in order to maximize the performance on a task, while satisfying given specifications (e.g., maximum cost of the resulting design). We propose a binary optimization formulation of the co-design problem and show that such formulation generalizes previous work based on strong modeling assumptions. We show that the proposed formulation can solve relatively large co-design problems in seconds and with minimal human intervention. We demonstrate the proposed approach in two applications: the co-design of an autonomous drone racing platform and the co-design of a multi-robot system

Maintenance optimization in industry 4.0

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A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities

International audienceIn this work, we propose a general modelling approach to estimate the life cycle cost of a system equipped with Prognostics and Health Management (PHM) capabilities, undergoing a Condition-Based Maintenance (CBM) policy. The approach builds on the Markov Chain theoretical framework, with transition probabilities linked to both PHM performance metrics of the literature and a novel metric. The developed approach can be used to guide economic decisions about CBM development, whichever the PHM algorithm is but provided that its performance metrics are estimated. The model is validated through a case study concerning a mechanical component of a train bogie affected by fatigue degradation, considering two different prognostic algorithms: Particle Filtering and a Model-Based approach

An Unsupervised Method for the Reconstruction of Maintenance Intervention Times

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Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning

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PLARIS: a Web Framework for Offering Automatically Classified Biomedical Multimedia Resources

PLARIS, a software environment connected to a streaming server, allows classifying all multimedia resources stored in the server. This allows searching into the media-pool with a controlled set of keywords. The system exploits generated data to automatically propose multimedia resources considered most relevant for the user and to obtain general learning paths. Focusing on biomedical resources, inside PLARIS we also implemented a section composed of some questions for each available resource, in order to offer the user the chance to test his/her comprehension of the related biomedical subject

Swarm Intelligence manuscript No. (Note: A revised version was published in Swarm Intelligence, 5(2):73–96) Self-organized Cooperation between Robotic Swarms

Abstract We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex an