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Background: Kangaroo Mother Care (KMC) requires few specialized resources, reduces mortality, and can be implemented in low resource environments. It is now recommended for implementation around the globe. Objectives: This paper discusses KMC use with low birth weight newborns at a Tanzania hospital providing valuable outcomes assessment of KMC use in rural areas of sub-Saharan Africa. Methods: The research used retrospective records (136 Low Birth Weight (LBW) and 33 Very Low Birth Weight (VLBW) ne- onates) supplemented by observations. Data included weights (longitudinal), survival period, and cause of death if it occurred. This hospital’s KMC use and study data began when the mother-baby dyad was referred, usually birth day. Results: This KMC group demonstrated 70% survival, but 77% of deaths occur within 24 hours. After the first 24 hours, KMC survival rate was 92%. Even VLBW neonates (

Author(s): Arrington, John; Brown, Ben; Beher, Steve; Cromaz, Mario; Dart, Eli; Edwards, Robert; Heyes, Graham; Jones, Clinton; Lauret, Jerome; Liddick, Sean; Mantica, Paul; Melo, Andrew; Monga, Inder; Morris, Brent; Pinkenburg, Chris; Porter, Jeff; Rai, Gulshan; Rockwell, Thomas; Rotman, Lauren; Simon, Richard; Stromsness, Rune; Wefel, Paul; Wiedlea, Andrew; Wilkinson, Sean; Winkler, Linda; Zurawski, Jason

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence., Comment: To appear in the journal of Future Generation Computer Systems

The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research project is building smart network services to accelerate scientific discovery in the era of 'big data' driven by Exascale, cloud computing, machine learning and AI. The project's architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. In addition, a highly intuitive 'intent' based interface, as defined by the project, allows applications to express their high-level service requirements, and an intelligent, scalable model-based software orchestrator converts that intent into appropriate network services, configured across multiple types of devices. The significance of these capabilities is the ability for science applications to manage the network as a first-class schedulable resource akin to instruments, compute, and storage, to enable well defined and highly tuned complex workflows that require close coupling of resources spread across a vast geographic footprint such as those used in science domains like high-energy physics and basic energy sciences.

This paper examines issues encountered attempting to exploit a high-bandwidth, high-latency link in support of a high-energy physics (HEP) analysis application. The primary issue was that the TCP additive increase/multiplicative decrease (AIMD) algorithm is not suitable for "long fat networks". While this is a known problem, the magnitude of the impact on application performance was much greater than anticipated. We were able to overcome much of the impact, by altering the AIMD coefficients. Such an approach, of course, is non-TCP compliant, and there was insufficient time to test the network friendliness of these modifications.

Cloud infrastructures have seen increasing popularity for addressing the growing computational needs of today's scientific and engineering applications. However, resource management challenges exist in the elastic cloud environment, such as resource provisioning and task allocation, especially when data movement between multiple domains plays an important role. In this work, we study the impact of data-aware resource management and scheduling on scientific workflows in multicloud environments. We develop a workflow simulator based on a network simulation framework for fine-grained simulation for workflow computation and data movement. Using the workload traces from a production metagenomic data analysis service, we evaluate different resource scheduling mechanisms, including proposed data-aware scheduling policies under various resource and bandwidth configurations. The results of this work are expected to answer questions about how to provision computing resources for certain workloads efficiently and how to place tasks across multidomain clouds in order to reduce data movement costs for overall improved system performance.

In preparation for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the climate community will run the Coupled Model Intercomparison Project phase 5 (CMIP-5) experiments, which are designed to answer crucial questions about future regional climate change and the results of carbon feedback for different mitigation scenarios. The CMIP-5 experiments will generate petabytes of data that must be replicated seamlessly, reliably, and quickly to hundreds of research teams around the globe. As an end-to-end test of the technologies that will be used to perform this task, a multidisciplinary team of researchers moved a small portion (10 TB) of the multimodel Coupled Model Intercomparison Project, Phase 3 data set used in the IPCC Fourth Assessment Report from three sources-the Argonne Leadership Computing Facility (ALCF) Lawrence Livermore National Laboratory (LLNL) and National Energy Research Scientific Computing Center (NERSC)-to the 2009 Supercomputing conference (SC09) show floor in Portland, Oregon, over circuits provided by DOE's ESnet. The team achieved a sustained data rate of 15 Gb/s on a 20 Gb/s network. More important, this effort provided critical feedback on how to deploy, tune, and monitor the middleware that will be used to replicate the upcoming petascale climate datasets. We report on obstacles overcome and the key lessons learned from this successful bandwidth challenge effort. Copyright 2010 ACM.

A GMPLS (Generalized Multi-Protocol Label Switching)-based link restoration scheme with IP-routing stability for data link failure between routers through a PXC (Photonic Cross-Connect) network was proposed and successfully demonstrated in a US nationwide photonic network for the first time.

The Lambda-on-demand functionality with link aggregation to accommodate the dynamic bandwidth demands of an ultra-high-resolution visualization application was realized in over a US nationwide photonic network for the first time.

Argonne National Laboratory, with facilities in the states of Illinois and Idaho, is owned by the United States government, and operated by The University of Chicago under the provisions of a contract with the Department of Energy.