Non-Arrhenius ionic conductivities in glasses due to a distribution of activation energies

Previously observed non-Arrhenius behavior in fast ion conducting glasses [\textit{Phys.\ Rev.\ Lett.}\ \textbf{76}, 70 (1996)] occurs at temperatures near the glass transition temperature, $T_{g}$, and is attributed to changes in the ion mobility due to ion trapping mechanisms that diminish the conductivity and result in a decreasing conductivity with increasing temperature. It is intuitive that disorder in glass will also result in a distribution of the activation energies (DAE) for ion conduction, which should increase the conductivity with increasing temperature, yet this has not been identified in the literature. In this paper, a series of high precision ionic conductivity measurements are reported for $0.5{Na}_{2}{S}+0.5[x{GeS}_{2}+(1-x){PS}_{5/2}]$ glasses with compositions ranging from $0 \leq x \leq 1$. The impact of the cation site disorder on the activation energy is identified and explained using a DAE model. The absence of the non-Arrhenius behavior in other glasses is explained and it is predicted which glasses are expected to accentuate the DAE effect on the ionic conductivity.Comment: 2 figure

Barriers to energy efficiency: evidence from selected sectors

To combat climate change, it is essential to reduce the use of fossil fuels and minimise greenhouse gas emissions. To help to achieve that objective, energy must be used efficiently. However, many international studies claim that companies and other organisations are “leaving money on the floor” by neglecting highly cost-effective opportunities to invest in measures that would improve their energy efficiency. A new ESRI report, “Barriers to Energy Efficiency: Evidence from Selected Sectors”, examines these claims in the context of the Irish economy, and asks why organisations apparently ignore financially rewarding opportunities to improve their energy efficiency. The report is based on detailed case studies of organisations in the mechanical engineering, brewing and higher education sectors

Recycling Wine Corks for Horticultural Use

No abstract

Everything you need, should, could, would want to know about data security.

PresentationA LIFT Forum presentation. The purpose of the Library and Information Technology Forum is to inform the University of Utah community about electronic information resources, and current trends in the use of computers and online technologies for accessing these resources

UA Research Summary No. 15

Utterly worthless. That’s how a congressman from Missouri described Alaska in 1867, when the U.S. bought it from Russia. A lot of Americans agreed. For almost 100 years, hardly anyone— except some Alaskans—wanted Alaska to become a state. But Alaska did finally become a state, in 1959. Today, after 142 years as a U.S. possession and 50 years as a state, Alaska has produced resources worth (in today’s dollars) around $670 billion. The U.S. paid$7.2 million for Alaska, equal to about $106 million now. For perspective, that’s roughly what the state government collected in royalties from oil produced on state-owned land in just the month of March 2009. To help mark 50 years of statehood, this publication first takes a broad look at what’s changed in Alaska since 1959. That’s on this page and the back page. We’ve also put together a timeline of political and economic events in Alaska from 1867 to the present. That’s on the inside pages. There’s an interactive version of the timeline—with photos, figures, and more—on ISER’s Web site: www.iser.uaa.alaska.edu

Issues in knowledge representation to support maintainability: A case study in scientific data preparation

Scientific data preparation is the process of extracting usable scientific data from raw instrument data. This task involves noise detection (and subsequent noise classification and flagging or removal), extracting data from compressed forms, and construction of derivative or aggregate data (e.g. spectral densities or running averages). A software system called PIPE provides intelligent assistance to users developing scientific data preparation plans using a programming language called Master Plumber. PIPE provides this assistance capability by using a process description to create a dependency model of the scientific data preparation plan. This dependency model can then be used to verify syntactic and semantic constraints on processing steps to perform limited plan validation. PIPE also provides capabilities for using this model to assist in debugging faulty data preparation plans. In this case, the process model is used to focus the developer's attention upon those processing steps and data elements that were used in computing the faulty output values. Finally, the dependency model of a plan can be used to perform plan optimization and runtime estimation. These capabilities allow scientists to spend less time developing data preparation procedures and more time on scientific analysis tasks. Because the scientific data processing modules (called fittings) evolve to match scientists' needs, issues regarding maintainability are of prime importance in PIPE. This paper describes the PIPE system and describes how issues in maintainability affected the knowledge representation used in PIPE to capture knowledge about the behavior of fittings