Hierarchical network topographical routing

Within the last 10 years the content consumption model that underlies many of the assumptions about traffic aggregation within the Internet has changed; the previous short burst transfer followed by longer periods of inactivity that allowed for statistical aggregation of traffic has been increasingly replaced by continuous data transfer models. Approaching this issue from a clean slate perspective; this work looks at the design of a network routing structure and supporting protocols for assisting in the delivery of large scale content services. Rather than approaching a content support model through existing IP models the work takes a fresh look at Internet routing through a hierarchical model in order to highlight the benefits that can be gained with a new structural Internet or through similar modifications to the existing IP model. The work is divided into three major sections: investigating the existing UK based Internet structure as compared to the traditional Autonomous System (AS) Internet structural model; a localised hierarchical network topographical routing model; and intelligent distributed localised service models. The work begins by looking at the United Kingdom (UK) Internet structure as an example of a current generation technical and economic model with shared access to the last mile connectivity and a large scale wholesale network between Internet Service Providers (ISPs) and the end user. This model combined with the Internet Protocol (IP) address allocation and transparency of the wholesale network results in an enforced inefficiency within the overall network restricting the ability of ISPs to collaborate. From this model a core / edge separation hierarchical virtual tree based routing protocol based on the physical network topography (layers 2 and 3) is developed to remove this enforced inefficiency by allowing direct management and control at the lowest levels of the network. This model acts as the base layer for further distributed intelligent services such as management and content delivery to enable both ISPs and third parties to actively collaborate and provide content from the most efficient source

High-performance time-series quantitative retrieval from satellite images on a GPU cluster

The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.N/

Investigating multi-material hydrogel three-dimensional printing for in vitro representation of the neo-vasculature of solid tumours : a comprehensive mechanical analysis and assessment of nitric oxide release from human umbilical vein endothelial cells

Many solid tumours (e.g. sarcoma, carcinoma and lymphoma) form a disorganized neo-vasculature that initiates uncontrolled vessel formation to support tumour growth. The complexity of these environments poses a significant challenge for tumour medicine research. While animal models are commonly used to address some of these challenges, they are time-consuming and raise ethical concerns. In vitro microphysiological systems have been explored as an alternative, but their production typically requires multi-step lithographic processes that limit their production. In this work, a novel approach to rapidly develop multi-material tissue-mimicking, cell-compatible platforms able to represent the complexity of a solid tumour's neo-vasculature is investigated via stereolithography three-dimensional printing. To do so, a series of acrylate resins that yield covalently photo-cross-linked hydrogels with healthy and diseased mechano-acoustic tissue-mimicking properties are designed and characterized. The potential viability of these materials to displace animal testing in preclinical research is assessed by studying the morphology, actin expression, focal adhesions and nitric oxide release of human umbilical vein endothelial cells. These materials are exploited to produce a simplified multi-material three-dimensional printed model of the neo-vasculature of a solid tumour, demonstrating the potential of our approach to replicate the complexity of solid tumours in vitro without the need for animal testing