The significant other: the value of jewellery within the conception, design and experience of body focussed digital devices

In this paper, we demonstrate how craft practice in contemporary jewellery opens up conceptions of ‘digital jewellery’ to possibilities beyond merely embedding pre-existing behaviours of digital systems in objects, which follow shallow interpretations of jewellery. We argue that a design approach that understands jewellery only in terms of location on the body is likely to lead to a world of ‘gadgets’, rather than anything that deserves the moniker ‘jewellery’. In contrast, by adopting a craft approach, we demonstrate that the space of digital jewellery can include objects where the digital functionality is integrated as one facet of an object that can be personally meaningful for the holder or wearer.</p

Machine Science in Biomedicine: Practicalities, Pitfalls and Potential

Machine Science, or Data-driven Research, is a new and interesting scientific methodology that uses advanced computational techniques to identify, retrieve, classify and analyse data in order to generate hypotheses and develop models. In this paper we describe three recent biomedical Machine Science studies, and use these to assess the current state of the art with specific emphasis on data mining, data assessment, costs, limitations, skills and tool support

A system overview of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

The AVIRIS instrument has been designed to do high spectral resolution remote sensing of the Earth. Utilizing both silicon and indium antimonide line array detectors, AVIRIS covers the spectral region from 0.41 to 2.45 microns in 10-nm bands. It was designed to fly aboard NASA's U-2 and ER-2 aircraft, where it will simulate the performance of future spacecraft instrumentation. Flying at an altitude of 20 km, it has an instantaneous field of view of 20 m and views a swath over 10 km wide. With an ability to record 40 minutes of data, it can, during a single flight, capture 500 km of flight line

ECONOMIC AND TECHNICAL ANALYSIS OF ETHANOL DRY MILLING: MODEL DESCRIPTION

Ethanol, the common name for ethyl alcohol, is fuel grade alcohol that is predominately produced through the fermentation of simple carbohydrates by yeasts. In the United States, the carbohydrate feedstock most commonly used in the commercial production of ethanol is yellow dent corn (YDC). The use of ethanol in combustion engines emits less greenhouse gasses than its petroleum equivalent, and it is widely hoped that the increased substitution of petroleum by ethanol will reduce US dependence on imported oil and decrease greenhouse gas emissions. Production of ethanol within the United States is expected to double, from 3.4 billion gallons in 2004, to about seven billion gallons in the next five years. Two processes currently being utilized to produce ethanol from YDC are dry milling and wet milling. The wet mill process is more versatile than the dry mill process in that it produces a greater variety of products; starch, corn syrup, ethanol, Splenda, etc., which allows for the wet mill to better react to market conditions. However, the costs of construction and operation of a wet mill are much greater than those of a dry mill. If ethanol is the target product, then it can be produced at a lower cost and more efficiently in a dry mill plant than in a wet mill plant, under current economic conditions. Of the more than 70 US ethanol plants currently in production, only a few are of the wet mill variety. A descriptive engineering spreadsheet model (DM model) was developed to model the dry mill ethanol production process. This model was created to better understand the economics of the ethanol dry mill production process and how the profitability of dry mill plants is affected under different conditions. It was also developed to determine the economic and environmental costs and benefits of utilizing new and different technologies in the dry mill process. Specifically, this model was constructed to conduct an economic analysis for novel processes of obtaining greater alcohol yields in the dry mill process by conducting a secondary fermentation of sugars converted from lignocellulosics found in the dry mill co-product, distiller’s grains. This research is being conducted at Purdue University, Michigan State, Iowa State, USDA, and NCAUR under a grant from the US Department of Energy. The DM model is more technically precise, and more transparent, than other models of the dry mill process that have been constructed for similar purposes. The Tiffany and Eidman model (TE model) uses broad generalities of the dry mill process, based on the current state of production, to approximate the sensitivities of the process to changes in variables. The TE model parameters were well researched, but the model suffers from several drawbacks. The main limitations of this model are that the results are very sensitive to the input values chosen by the user. Unlike the DM model, complex manipulations, such as determining the effect of new technologies would require accurate parameter estimates using the TE model. The McAloon model [11].uses highly technical engineering software (ASPEN) that acts essentially as a “black box” in the dry mill production process. This very exact model does not allow for a more general examination of the dry mill process. Changes in the production process would necessitate precise engineering plans. Similar to the TE and McAloon models, the DM model is a spreadsheet model, but unlike the McAloon model it is completely self-contained. The DM model is a feed backward model, input requirements (corn, enzymes, chemicals, utilities, etc) are calculated based on the user entered values for annual production and process parameters. The mass flow rates, in pounds per hour were then calculated and used in estimating the size, in dimension or power, of each major piece of equipment. The cost associated with each piece of major equipment was then calculated as an exponential function of its corresponding size. Total capital costs associated with a dry mill plant were then estimated using the percentage of equipment costs method [13]. It was found that the DM model estimates of the total capital costs associated with medium to large dry mill plants (those with the capacity to produce between 10 and 100 million gallons of ethanol a year) were within 5% of total fixed costs estimated by BBI [2]. Operating costs were compared with the 2002 USDA survey results and also found to be very close [15]. A companion document, “Economic and Technical Analysis of Dry Milling: Model User’s Manual,” staff paper no 06-05, explains how the model is used to conduct analysis of dry milling alternatives.Ethanol, DDGS, Dry Milling, Biochemical Process Engineering, Economic Modeling, Financing, Fermentation Process Modeling

Observation of Single Transits in Supercooled Monatomic Liquids

A transit is the motion of a system from one many-particle potential energy valley to another. We report the observation of transits in molecular dynamics (MD) calculations of supercooled liquid argon and sodium. Each transit is a correlated simultaneous shift in the equilibrium positions of a small local group of particles, as revealed in the fluctuating graphs of the particle coordinates versus time. This is the first reported direct observation of transit motion in a monatomic liquid in thermal equilibrium. We found transits involving 2 to 11 particles, having mean shift in equilibrium position on the order of 0.4 R_1 in argon and 0.25 R_1 in sodium, where R_1 is the nearest neighbor distance. The time it takes for a transit to occur is approximately one mean vibrational period, confirming that transits are fast.Comment: 19 pages, 8 figure

Review: Do the Different Sensory Areas within the Cat Anterior Ectosylvian Sulcal Cortex Collectively Represent a Network Multisensory Hub?

Current theory supports that the numerous functional areas of the cerebral cortex are organized and function as a network. Using connectional databases and computational approaches, the cerebral network has been demonstrated to exhibit a hierarchical structure composed of areas, clusters and, ultimately, hubs. Hubs are highly connected, higher-order regions that also facilitate communication between different sensory modalities. One region computationally identified network hub is the visual area of the Anterior Ectosylvian Sulcal cortex (AESc) of the cat. The Anterior Ectosylvian Visual area (AEV) is but one component of the AESc that also includes the auditory (Field of the Anterior Ectosylvian Sulcus - FAES) and somatosensory (Fourth somatosensory representation - SIV). To better understand the nature of cortical network hubs, the present report reviews the biological features of the AESc. Within the AESc, each area has extensive external cortical connections as well as among one another. Each of these core representations is separated by a transition zone characterized by bimodal neurons that share sensory properties of both adjoining core areas. Finally, core and transition zones are underlain by a continuous sheet of layer 5 neurons that project to common output structures. Altogether, these shared properties suggest that the collective AESc region represents a multiple sensory/multisensory cortical network hub. Ultimately, such an interconnected, composite structure adds complexity and biological detail to the understanding of cortical network hubs and their function in cortical processing

Muon spin relaxation and rotation study on the solid solution of the two spin-gap systems (CH3)2CHNH3-CuCl3 and (CH3)2CHNH3-CuBr3

Muon-spin-rotation and relaxation studies have been performed on (CH$_3$)$_2$CHNH$_3$Cu(Cl$_x$Br$_{1-x}$)$_3$ with $x$=0.85 and 0.95, which are solid solutions of the two isomorphic spin-gap systems (CH$_3$)$_2$CHNH$_3$CuCl$_3$ and (CH$_3$)$_2$CHNH$_3$CuBr$_3$ with different spin gaps. The sample with $x$=0.85 showed a clear muon spin rotation under zero-field below $T_{\rm N}$=11.65K, indicating the existence of a long-range antiferromagnetic order. A critical exponent of the hyperfine field was obtained to be $\beta$=0.33, which agrees with 3D-Ising model. In the other sample with $x$=0.95, an anomalous enhancement of the muon spin relaxation was observed at very low temperatures indicating a critical slowing down due to a magnetic instability of the ground state