A novel method for the absolute fluorescence yield measurement by AIRFLY

One of the goals of the AIRFLY (AIR FLuorescence Yield) experiment is to measure the absolute fluorescence yield induced by electrons in air to better than 10% precision. We introduce a new technique for measurement of the absolute fluorescence yield of the 337 nm line that has the advantage of reducing the systematic uncertainty due to the detector calibration. The principle is to compare the measured fluorescence yield to a well known process - the Cerenkov emission. Preliminary measurements taken in the BFT (Beam Test Facility) in Frascati, Italy with 350 MeV electrons are presented. Beam tests in the Argonne Wakefield Accelerator at the Argonne National Laboratory, USA with 14 MeV electrons have also shown that this technique can be applied at lower energies.Comment: presented at the 5th Fluorescence Workshop, El Escorial - Madrid, Spain, 16 - 20 September 200

First cross-correlation analysis of interferometric and resonant-bar gravitational-wave data for stochastic backgrounds

Data from the LIGO Livingston interferometer and the ALLEGRO resonant-bar detector, taken during LIGO's fourth science run, were examined for cross correlations indicative of a stochastic gravitational-wave background in the frequency range 850-950 Hz, with most of the sensitivity arising between 905 and 925 Hz. ALLEGRO was operated in three different orientations during the experiment to modulate the relative sign of gravitational-wave and environmental correlations. No statistically significant correlations were seen in any of the orientations, and the results were used to set a Bayesian 90% confidence level upper limit of Ωgw(f)≤1.02, which corresponds to a gravitational-wave strain at 915 Hz of 1.5×10-23Hz-1/2. In the traditional units of h1002Ωgw(f), this is a limit of 0.53, 2 orders of magnitude better than the previous direct limit at these frequencies. The method was also validated with successful extraction of simulated signals injected in hardware and software. © 2007 The American Physical Society

Multi-Objective Optimization Methods for Designing Low-Carbon Concrete Mixtures

Concrete mixtures are complex material systems with a multitude of characteristics that decision-makers may deem important. These characteristics can include economic, environmental, mechanical, and durability-related properties of a concrete mixture. However, traditional concrete mixture design typically employs long-standing heuristics, which satisfy requirements for physical characteristics but are unable to minimize specific characteristics, such as the cost or carbon footprint of the concrete mixture. This work considers these performance characteristics by implementing simulation-optimization as a new paradigm for designing concrete mixtures. The utility of the simulation-optimization framework is tested for several concrete design case studies that simultaneously consider compressive strength, embodied carbon, service life, and cost. Results from these scenarios demonstrate that the local conditions of the case study dictate the most important parameters of the simulation-optimization (i.e., relative constituent costs, in situ service-life conditions). Out of all other parameters, constituent cost and service-life conditions impact the set of optimal concrete mixture designs in terms of the types and quantities of mixture ingredients that are utilized. We present a simulation-optimization framework that is demonstrated herein to be a holistic design tool that allows designers to quantify and visualize tradeoffs between critical concrete performance metrics. Such a tool can be used to precision-tailor low-carbon concrete mixtures to the exact preferences of the designer.</jats:p

Multi-Objective Optimization Methods for Designing Low-Carbon Concrete Mixtures

&lt;jats:p&gt;Concrete mixtures are complex material systems with a multitude of characteristics that decision-makers may deem important. These characteristics can include economic, environmental, mechanical, and durability-related properties of a concrete mixture. However, traditional concrete mixture design typically employs long-standing heuristics, which satisfy requirements for physical characteristics but are unable to minimize specific characteristics, such as the cost or carbon footprint of the concrete mixture. This work considers these performance characteristics by implementing simulation-optimization as a new paradigm for designing concrete mixtures. The utility of the simulation-optimization framework is tested for several concrete design case studies that simultaneously consider compressive strength, embodied carbon, service life, and cost. Results from these scenarios demonstrate that the local conditions of the case study dictate the most important parameters of the simulation-optimization (i.e., relative constituent costs, &lt;jats:italic&gt;in situ&lt;/jats:italic&gt; service-life conditions). Out of all other parameters, constituent cost and service-life conditions impact the set of optimal concrete mixture designs in terms of the types and quantities of mixture ingredients that are utilized. We present a simulation-optimization framework that is demonstrated herein to be a holistic design tool that allows designers to quantify and visualize tradeoffs between critical concrete performance metrics. Such a tool can be used to precision-tailor low-carbon concrete mixtures to the exact preferences of the designer.&lt;/jats:p&gt

Geographic and temporal variation in the consumption of bats by European barn owls

Capsule We report a review of the occurrence of bats in the Barn Owl diet Tyto alba in Europe. Based on 802 studies reporting 4.02 million prey items identified in pellets, 4949 were bats (0.12%). We found that bat predation decreased during the last 150 years, is more frequent on islands than mainland, and is higher in eastern than western Europe and in southern than northern Europe. Although Barn Owls usually capture bats opportunistically, they can sometimes specialize on them