Microstructure property relationship in Pyrex glass composites reinforced with Nicalon fibres

Detailed microstructural studies have been carried out on a series of composites consisting of Pyrex glass reinforced with Nicalon fibres. A variety of techniques has been employed, including X-ray and electron diffraction, electron-probe microanalysis and thin foil analytical electron microscopy. In parallel, mechanical tests have been performed on the composites and measurements have been made of the fibre-matrix bond.Substantial amounts of cristobalite have been identified in the matrix, up to 48% by volume in some cases. At such levels, microcracking is a common occurrence due to the high differential contraction between the matrix constituents upon cooling, which leads to matrix disintegration upon mechanical testing. A second microstructural feature which affects the mechanical behaviour of the composite concerns the fibre-matrix interface and, in particular, the chemistry of the outermost (200 nm) surface regions of the fibre. The amount of graphite here is shown to affect directly the strength of the fibre-matrix bond and, in turn, the degree of fibre pull out and the mechanical properties of the composite.     

Archival Appraisal and the Digital Record: Applying Past Tradition for Future Practice

The digital revolution has changed the daily work of archivists. As a result, archivists have refined their analog practices and initiated new ones to accommodate the unique information creation and access needs of users. While limitless storage and technological obsolescence may be surmountable challenges, appraisal continues to be a necessary component of this preservation enterprise. Macro-appraisal—a top-down functional assessment of an organization’s high-level decision making that defines the enduring value of specific bodies of documentary evidence of these creative or administrative processes—is used to facilitate the management of diverse bodies of born-digital records in archives and special collections.     

Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach

Traditionally, model calibration is formulated as a single objective problem, where fidelity to measurements is maximized by adjusting model parameters. In such a formulation however, the model with best fidelity merely represents an optimum compromise between various forms of errors and uncertainties and thus, multiple calibrated models can be found to demonstrate comparable fidelity producing non-unique solutions. To alleviate this problem, the authors formulate model calibration as a multi-objective problem with two distinct objectives: fidelity and robustness. Herein, robustness is defined as the maximum allowable uncertainty in calibrating model parameters with which the model continues to yield acceptable agreement with measurements. The proposed approach is demonstrated through the calibration of a finite element model of a steel moment resisting frame.     

DB2 Universal Database: A Case Study of a Successful User-Centered Design Program

This case study describes the application of user-centered design (UCD) principles to the development of a complex middleware software product-IBM's DB2(r) Universal Database. Motivated by trade press reviews highlighting ease-of-use problems, a multisite development team utilized IBM UCD to transform ease of use into a featured product attribute. This case study describes the initial application of UCD to DB21 and the positive business results that followed: critical acclaim, increased marketshare and user satisfaction, and increased support and adoption of UCD across the IBM database product family. Although ease-of-use objectives, target markets, and human factors practitioners' roles have evolved over 6 years and multiple versions of the product, DB2 continues to achieve positive results with UCD. In addition to the flexibility of the UCD methodology, other factors that influenced DB2's success included management support, resource commitment, cross-site communication and collaboration, detailed product specifications, and a focus on supporting user tasks.     

Report from the Wellcome Trust/EBA “Perspectives in Clinical Proteomics” retreat--a strategy to implement next-generation proteomic analyses to the clinic for patient benefit: pathway to translation

Proteomics has long been thought to hold the promise of producing results of clinical utility which will influence patient treatment and outcomes. A recent Wellcome Trust/EBI meeting and retreat--“Perspectives in Clinical Proteomics”--brought together experts from a broad range of stakeholder groups with an interest in ensuring proteomics achieves this aim. This viewpoint presents views derived from these forums, proposing a pathway for the development of next-generation proteomic analyses in the clinical setting from selection of candidates through to their validation and ultimate demonstration of utility through health technology assessments. Although not meant to be all encompassing, important elements for proteomics researchers to consider are presented.     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

Knowledge-directed Adaptation in Multi-level Agents

Most work on adaptive agents have a simple, single layerarchitecture. However, most agent architectures support three levels ofknowledge and control: a reflex level for reactive responses, a deliberatelevel for goal-driven behavior, and a reflective layer for deliberateplanning and problem decomposition. In this paper we explore agentsimplemented in Soar that behave and learn at the deliberate and reflectivelevels. These levels enhance not only behavior, but also adaptation. Theagents use a combination of analytic and empirical learning, drawing from avariety of sources of knowledge to adapt to their environment. We hypothesize that complete, adaptive agents must be able to learn across all three levels.