Effect of aluminum on the low temperature properties of relatively high purity ferrite

True stress-strain data on alloys of pure iron with up to 2.4 pct Al were obtained in the temperature range +100° to ?185°C. Aluminum was found to reduce yield and flow stresses of iron at low temperatures but to have little or no effect on ductility. The effects of temperature and composition on strain hardening are discussed.     

The lesbian,gay, bisexual and transgender community online: discussions of bullying and self-disclosure in YouTube videos

Computer-mediated communication has become a popular platform for identity construction and experimentation as well as social interaction for those who identify as lesbian, gay, bisexual or transgender (LGBT). The creation of user-generated videos has allowed content creators to share experiences on LGBT topics. With bullying becoming more common amongst LGBT youth, it is important to obtain a greater understanding of this phenomenon. In our study, we report on the analysis of 151 YouTube videos which were identified as having LGBT- and bullying-related content. The analysis reveals how content creators openly disclose personal information about themselves and their experiences in a non-anonymous rhetoric with an unknown public. These disclosures could indicate a desire to seek friendship, support and provide empathy.     

Calculation of film stress from pitch measurements in LCD panel manufacturing

A method based on inverting a finite element model is presented for determining film stress from pitch changes before and after a film deposition step in liquid‐crystal display panel manufacturing. It differs from the conventional methods by making use of in‐plane deformation rather than out‐of‐plane measurements to calculate film stress. The resulting film stress is confounded with glass structural relaxation. Measurements of out‐of‐plane deformation at the edge of the sheet can be used with the pitch measurements to separate the effects of glass structural relaxation and film stress.     

Evolutionary search for understanding movement dynamics on mixed networks

This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis.     

'Tacitus' project: identifying multi-sensory perceptions in creative 3D practice for the development of a haptic computing system for applied artists

This paper outlines the major motivating factors concerning a novel collaborative project between Edinburgh College of Art and Edinburgh Virtual Environment Centre. The Tacitus project will investigate the use of multi-modal virtual environments, specifically, the haptic (touch) modality, with regards to the creative processes employed by designers working within the field of applied arts. The salient areas of research are described, and the methods by which information regarding these areas will be obtained are considered. Initial investigations have revealed a strong need to mimic the traditional applied artists' workspaces, with co-location of visual and haptic cues a priority.     

Using non-ideal gates to implement universal quantum computing between uncoupled qubits

In many physical systems, when implementing quantum gate operations unavoidable global and relative phases occur as by-products due to the internal structure of the governing Hamiltonian. To correct, additional phase rotation gates are used, which increases the computational overhead. Here, we show how these phase by-products can actually be used to our advantage by using them to implement universal quantum computing between qubits not directly coupled to each other. The gate operations, CNOT, Toffoli, and swap gates, require much less computational overhead than present schemes, and are achieved with fidelity greater than 99%. We then present a linear nearest-neighbor architecture that takes full advantage of the phase by-products, and we show how to implement gates from a universal set efficiently in this layout. In this scheme gate operations are realized by only varying a single control parameter per data qubit, and the ability to tune couplings is not required.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.