Optimum structural design with static aeroelastic constraints

The static aeroelastic performance characteristics, divergence velocity, control effectiveness and lift effectiveness are considered in obtaining an optimum weight structure. A typical swept wing structure is used with upper and lower skins, spar and rib thicknesses, and spar cap and vertical post cross-sectional areas as the design parameters. Incompressible aerodynamic strip theory is used to derive the constraint formulations, and aerodynamic load matrices. A Sequential Unconstrained Minimization Technique (SUMT) algorithm is used to optimize the wing structure to meet the desired performance constraints

Modulation of La Crosse Virus Infection in Aedes albopictus Mosquitoes Following Larval Exposure to Coffee Extracts

The mosquito-borne La Crosse virus (LACV; Family Bunyaviridae) may cause encephalitis, primarily in children, and is distributed throughout much of the eastern United States. No antivirals or vaccines are available for LACV, or most other mosquito-borne viruses, and prevention generally relies on mosquito control. We sought to determine whether coffee extracts could interfere with LACV replication and vector mosquito development. Both regular and decaffeinated coffee demonstrated significant reductions in LACV replication in direct antiviral assays. This activity was not due to the presence of caffeine, which did not inhibit the virus life cycle. Aedes albopictus (Skuse; Diptera: Culicidae) mosquito larvae suffered near total mortality when reared in high concentrations of regular and decaffeinated coffee and in caffeine. Following larval exposure to sublethal coffee concentrations, adult A. albopictus mosquitoes had significantly reduced whole-body LACV titers 5 days post-infection, compared to larvae reared in distilled water. These results suggest that it may be possible to both control mosquito populations and alter the vector competence of mosquitoes for arthropod-borne viruses by introducing antiviral compounds into the larval habitat

Graphite: A Distributed Parallel Simulator for Multicores

This paper introduces the open-source Graphite distributed parallel multicore simulator infrastructure. Graphite is designed from the ground up for exploration of future multicore processors containing dozens, hundreds, or even thousands of cores. It provides high performance for fast design space exploration and software development for future processors. Several techniques are used to achieve this performance including: direct execution, multi-machine distribution, analytical modeling, and lax synchronization. Graphite is capable of accelerating simulations by leveraging several machines. It can distribute simulation of an off-the-shelf threaded application across a cluster of commodity Linux machines with no modification to the source code. It does this by providing a single, shared address space and consistent single-process image across machines. Graphite is designed to be a simulation framework, allowing different component models to be easily replaced to either model different architectures or tradeoff accuracy for performance. We evaluate Graphite from a number of perspectives and demonstrate that it can simulate target architectures containing over 1000 cores on ten 8-core servers. Performance scales well as more machines are added with near linear speedup in many cases. Simulation slowdown is as low as 41x versus native execution for some applications. The Graphite infrastructure and existing models will be released as open-source software to allow the community to simulate their own architectures and extend and improve the framework

An Intrinsic Description of the Nonlinear Aeroelasticity of Very Flexible Wings

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90662/1/AIAA-2011-1917-972.pd

Enabling technologies for self-aware adaptive systems

Self-aware computer systems will be capable of adapting their behavior and resources thousands of times a second to automatically find the best way to accomplish a given goal despite changing environmental conditions and demands. Such a capability benefits a broad spectrum of computer systems from embedded systems to supercomputers and is particularly useful for meeting power, performance, and resource-metering challenges in mobile computing, cloud computing, multicore computing, adaptive and dynamic compilation environments, and parallel operating systems. Some of the challenges in implementing self-aware systems are a) knowing within the system what the goals of applications are and if they are meeting them, b) deciding what actions to take to help applications meet their goals, and c) developing standard techniques that generalize and can be applied to a broad range of self-aware systems. This work presents our vision for self-aware adaptive systems and proposes enabling technologies to address these three challenges. We describe a framework called Application Heartbeats that provides a general, standardized way for applications to monitor their performance and make that information available to external observers. Then, through a study of a self-optimizing synchronization library called Smartlocks, we demonstrate a powerful technique that systems can use to determine which optimization actions to take. We show that Heartbeats can be applied naturally in the context of reinforcement learning optimization strategies as a reward signal and that, using such a strategy, Smartlocks are able to significantly improve performance of applications on an important emerging class of multicore systems called asymmetric multicores.Roberto Rocca Foundatio