Randomized flow model and centrality measure for electrical power transmission network analysis

International audienceCommonly used centrality measures identify the most important elements in networks of components, based on the assumption that flow occurs in the network only along the shortest paths. This is not so in real networks, where different operational rules drive the flow. For this reason, a different model of flow in a network is considered here: rather than along shortest paths only, it is assumed that contributions come essentially from all paths between nodes, as simulated by random walks. Centrality measures can then be coherently defined. An example of application to an electrical power transmission system is presented

Super telescopic catheter system parallel to a contralateral stiff guide wire to cross extremely complex pulmonary arteries

Access to complex stenotic pulmonary arteries can be challenging due to their anatomy or secondary to prior multiple surgeries and interventions. Two techniques have been previously described to address this issue: the telescopic catheter-in-long sheath parallel to a stiff guidewire technique and the use of a microcatheter in a telescopic scope. We integrated and modified these techniques creating a super telescopic system with a SuperCross (R) microcatheter-in-catheter-in-long sheath, parallel to a contralateral stiff guidewire to access a previously repaired and stented left pulmonary artery. The stiff wire support and the 90 degrees flexiblity of the Supercross (R) microcatheter assembled coaxial to the diagnostic catheter and the long sheath contributed to the successful ballooning and stenting-in-stent of the pulmonary artery

A stochastic framework for uncertainty analysis in electric power transmission systems with wind generation

International audienceThe purpose of this work is the analysis of the uncertainties affecting an electric transmission network with wind power generation and their impact on its reliability. A stochastic model was developed to simulate the operations and the line disconnection and reconnection events of the electric network due to overloads beyond the rated capacity. We represent and propagate the uncertainties related to consumption variability, ambient temperature variability, wind speed variability and wind power generation variability. The model is applied to a case study of literature. Conclusions are drawn on the impact that different sources of variability have on the reliability of the network and on the seamless electric power supply. Finally, the analysis enables identifying possible system states, in terms of power request and supply, that are critical for network vulnerability and may induce a cascade of line disconnections leading to massive network blackout

Performance analysis of a power transmission system under uncertain load conditions and network configurations

International audienceIn this paper, the load flow problem in a power transmission network is studied in presence of load and power generation uncertainties and transmission lines failures. Network performance indicators are computed and the importance of the different components is evaluated by a power flow betwenness centrality measure

Transcatheter Closure of a Secundum Atrial Septal Defect with Deficient Aortic Rim Through the Left Internal Jugular Vein in a Child with Situs Inversus and Interrupted Inferior Vena Cava: Device's Choice Matters

Percutaneous closure of secundum atrial septal defect (sASD) in children with interrupted inferior vena cava is challenging, especially in case of deficient aortic rim. Trans-jugular access is generally preferred in this scenario. Patients with situs inversus and sASD also carry technical difficulties for transcatheter closure because of the orientation of the atrial septum. We report a successful case of percutaneous closure of a sASD with deficient aortic rim using an occlutech figulla flex II ASD device through the left internal jugular vein in a child with situs inversus, dextrocardia, and interrupted IVC. This case was facilitated by the absence of left-sided hub of the Occlutech device to provide stable opening of the device into the left atrium, whereas the ball-connection of the delivery system allowed an angle of almost 180 degrees between the device and the atrial septum

Soft Tissue Simulation Environment to Learn Manipulation Tasks in Autonomous Robotic Surgery

Reinforcement Learning (RL) methods have demonstrated promising results for the automation of subtasks in surgical robotic systems. Since many trial and error attempts are required to learn the optimal control policy, RL agent training can be performed in simulation and the learned behavior can be then deployed in real environments. In this work, we introduce an open-source simulation environment providing support for position based dynamics soft bodies simulation and state-of-the-art RL methods. We demonstrate the capabilities of the proposed framework by training an RL agent based on Proximal Policy Optimization in fat tissue manipulation for tumor exposure during a nephrectomy procedure. Leveraging on a preliminary optimization of the simulation parameters, we show that our agent is able to learn the task on a virtual replica of the anatomical environment. The learned behavior is robust to changes in the initial end-effector position. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the RL agent. The proposed simulation environment represents an essential component for the development of next-generation robotic systems, where the interaction with the deformable anatomical environment is involved

Learning State-Variable Relationships in POMCP: A Framework for Mobile Robots

We address the problem of learning relationships on state variables in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and represent the acquired knowledge with a Markov Random Field (MRF). We propose, in particular, a method for learning these relationships on a robot as POMCP is used to plan future actions. Then, we present an algorithm that deals with cases in which the MRF is used on episodes having unlikely states with respect to the equality relationships represented by the MRF. Our approach acquires information from the agent’s action outcomes to adapt online the MRF if a mismatch is detected between the MRF and the true state. We test this technique on two domains, rocksample, a standard rover exploration task, and a problem of velocity regulation in industrial mobile robotic platforms, showing that the MRF adaptation algorithm improves the planning performance with respect to the standard approach, which does not adapt the MRF online. Finally, a ROS-based architecture is proposed, which allows running the MRF learning, the MRF adaptation, and MRF usage in POMCP on real robotic platforms. In this case, we successfully tested the architecture on a Gazebo simulator of rocksample. A video of the experiments is available in the Supplementary Material, and the code of the ROS-based architecture is available online

Ln(III) Complexes Embedded in Biocompatible PLGA Nanoparticles as Potential Vis-to-NIR Optical Probes

In this contribution, we present the spectroscopic study of two NIR emitting hydrophobic heteroleptic (R,R)-YbL1(tta) and (R,R)-NdL1(tta) complexes (with tta = 2-thenoyltrifluoroacetonate and L1 = N,N0 -bis(2-(8-hydroxyquinolinate)methylidene)-1,2-(R,R or S,S)-cyclohexanediamine), both in methanol solution and embedded in water dispersible and biocompatible poly lactic-co-glycolic acid (PLGA) nanoparticles. Thanks to their absorption properties in a wide range of wavelengths extending from the UV up to the blue and green visible regions, the emission of these complexes can be effectively sensitized using visible radiation, which is much less harmful to tissues and skin than the UV one. The encapsulation of the two Ln(III)-based complexes in PLGA allows us to preserve their nature, making them stable in water and to test their cytotoxicity on two different cell lines, with the aim of using them in the future as potential bioimaging optical probes

Near Infared Circularly Polarized Luminescence from water stable organic nanoparticles containing a chiral Yb(III) complex

We report the first example of very efficient NIR Circularly Polarized Luminescence (CPL) (around 970 nm) in water, obtained thanks to the combined use of a chiral Yb complex and of poly lactic-co-glycolic acid (PLGA) nanoparticles. [Yb L (tta) 2 ]CH 3 COO ( L = N, N'-bis(2-pyridylmethylidene)-1,2-( R,R + S,S ) cyclohexanediamine and tta = 2-thenoyltrifluoroacetonate) shows good CPL in organic solvents, because the tta ligands efficiently sensitize Yb NIR luminescence and the readily prepared chiral ligand L endows the complex with the necessary dissymmetry. PLGA nanoparticles incorporate the complex and protect the metal ion from the intrusion of solvent molecules, while ensuring biocompatibility, water solubility and stability to the complex. Hydrophilic NIR-CPL optical probes can find applications in the field of NIR-CPL bio-assays