Numerical simulation of blood flow and pressure drop in the pulmonary arterial and venous circulation

A novel multiscale mathematical and computational model of the pulmonary circulation is presented and used to analyse both arterial and venous pressure and flow. This work is a major advance over previous studies by Olufsen et al. (Ann Biomed Eng 28:1281–1299, 2012) which only considered the arterial circulation. For the first three generations of vessels within the pulmonary circulation, geometry is specified from patient-specific measurements obtained using magnetic resonance imaging (MRI). Blood flow and pressure in the larger arteries and veins are predicted using a nonlinear, cross-sectional-area-averaged system of equations for a Newtonian fluid in an elastic tube. Inflow into the main pulmonary artery is obtained from MRI measurements, while pressure entering the left atrium from the main pulmonary vein is kept constant at the normal mean value of 2 mmHg. Each terminal vessel in the network of ‘large’ arteries is connected to its corresponding terminal vein via a network of vessels representing the vascular bed of smaller arteries and veins. We develop and implement an algorithm to calculate the admittance of each vascular bed, using bifurcating structured trees and recursion. The structured-tree models take into account the geometry and material properties of the ‘smaller’ arteries and veins of radii ≥ 50 μ m. We study the effects on flow and pressure associated with three classes of pulmonary hypertension expressed via stiffening of larger and smaller vessels, and vascular rarefaction. The results of simulating these pathological conditions are in agreement with clinical observations, showing that the model has potential for assisting with diagnosis and treatment for circulatory diseases within the lung

Smart grid communication and networking technologies: recent developments and future challenges

The smart grid is ostensibly the next generation power grid in which electrical energy distribution and management is efficiently performed by exploiting information communication technologies such as pervasive computing, in the control and decision-making processes. The smart grid is characterised by such functionality as being able to adapt to load and demand changes, intelligently manage bidirectional data flow and crucially enhance system reliability, robustness, security and sustainability. Communication networks play a crucial role in facilitating these features and are an integral component in any smart grid management system. In this chapter, the role of the communications network in smart grid operation is described together with its main functionalities. In particular, the challenges and opportunities for integrating existing and future wireless and mobile networks into the smart grid will be analysed, while the chapter concludes by identifying some future research directions for smart grid technologies

Data and computation movement in fog environments: The DITAS approach

none11siData-intensive applications are becoming very important in several domains including e-health, government 2.0, smart cities, and industry 4.0. In fact, the significant increase of sensor deployment in the Internet of things (IoT) environments, in conjunction with the huge amount of data that are generated by the smart and intelligent devices such as smartphones, requires proper data management. The goal of this chapter is to focus on how to improve data management when data are produced and consumed in a Fog Computing environment, where both resources at the edge of the network (e.g., sensors and mobile devices) and resources in the cloud (e.g., virtual machines) are involved and need to operate seamlessly together. Based on the approach proposed in the European DITAS project, data and computation movement between the edge and the cloud are studied, to create a balance between such characteristics as latency and response time (when data are stored in edge-located resources) and scalability and reliability in case of data residing in the cloud. In this contribution, to enable data and computation movement, an approach based on the principles of Service-Oriented Computing applied to a Fog environment has been adopted.nonePlebani, Pierluigi*; Garcia-Perez, David; Anderson, Maya; Bermbach, David; Cappiello, Cinzia; Kat, Ronen I.; Marinakis, Achilleas; Moulos, Vrettos; Pallas, Frank; Tai, Stefan; Vitali, MonicaPlebani, Pierluigi; Garcia-Perez, David; Anderson, Maya; Bermbach, David; Cappiello, Cinzia; Kat, Ronen I.; Marinakis, Achilleas; Moulos, Vrettos; Pallas, Frank; Tai, Stefan; Vitali, Monic