Statistical analyses to support guidelines for marine avian sampling: Final report. [Digital Supplements A-G attached]

Interest in development of offshore renewable energy facilities has led to a need for high-quality, statistically robust information on marine wildlife distributions. A practical approach is described to estimate the amount of sampling effort required to have sufficient statistical power to identify species specific “hotspots” and “coldspots” of marine bird abundance and occurrence in an offshore environment divided into discrete spatial units (e.g., lease blocks), where “hotspots” and “coldspots” are defined relative to a reference (e.g., regional) mean abundance and/or occurrence probability for each species of interest. For example, a location with average abundance or occurrence that is three times larger the mean (3x effect size) could be defined as a “hotspot,” and a location that is three times smaller than the mean (1/3x effect size) as a “coldspot.” The choice of the effect size used to define hot and coldspots will generally depend on a combination of ecological and regulatory considerations. A method is also developed for testing the statistical significance of possible hotspots and coldspots. Both methods are illustrated with historical seabird survey data from the USGS Avian Compendium Database.

Fabrication and testing of a high resolution extensometer based on resonant MEMS strain sensors

A novel type of linear extensometer with exceptionally high resolution of 4 nm based on MEMS resonant strain sensors bonded on steel and operating in a vacuum package is presented. The tool is implemented by means of a steel thin bar that can be pre-stressed in tension within two fixing anchors. The extension of the bar is detected by using two vacuum-packaged resonant MEMS double- ended tuning fork (DETF) sensors bonded on the bar with epoxy glue, one of which is utilized for temperature compensation. Both sensors are driven by a closed loop self-oscillating transresistance amplifier feedback scheme implemented on a PCB (Printed Circuit Board). On the same board, a microcontroller-based frequency measurement circuit is also implemented, which is able to count the square wave fronts of the MEMS oscillator output with a resolution of 20 nsec. The system provides a frequency noise of 0.2 Hz corresponding to an extension resolution of 4 nm for the extensometer. Nearly perfect temperature compensation of the frequency output is achieved in the temperature range 20-35 C using the reference sensor. © 2011 IEEE.

A self-regulating valve for single-phase liquid cooling of microelectronics

This paper reports on the design, optimization and testing of a self-regulating valve for single-phase liquid cooling of microelectronics. Its purpose is to maintain the integrated circuit (IC) at constant temperature and to reduce power consumption by diminishing flow generated by the pump as a function of the cooling requirements. It uses a thermopneumatic actuation principle that combines the advantages of zero power consumption and small size in combination with a high flow rate and low manufacturing costs. The valve actuation is provided by the thermal expansion of a liquid (actuation fluid) which, at the same time, actuates the valve and provides feed-back sensing. A maximum flow rate of 38 kg h-1 passes through the valve for a heat load up to 500 W. The valve is able to reduce the pumping power by up to 60% and it has the capability to maintain the IC at a more uniform temperature. © 2011 IOP Publishing Ltd.

Compression and tensile properties of self-reinforced poly(ethylene terephthalate)-composites

Tensile and compression properties of self-reinforced poly(ethylene terephthalate) (SrPET) composites has been investigated. SrPET composites or all-polymer composites have improved mechanical properties compared to the bulk polymer but with maintained recyclability. In contrast to traditional carbon/glass fibre reinforced composites, SrPET composites are very ductile, resulting in high failure strains without softening or catastrophic failure. In tension, the SrPET composites behave linear elastically until the fibre-matrix interface fails, at which point the stiffness starts decreasing. As the material is further strained, strain hardening occurs and the specimen finally fails at a global strain above 10%. In compression, the composite initially fails through fibre yielding, and at higher strains through fibre bending. The stress-strain response is reminiscent of an elastic-perfectly plastic material with a high strain to failure (typically over 10%). This indicates that SrPET composites are not only candidates as semi-structural composites but also as highly efficient energy absorbing materials. © 2012 Elsevier Ltd. All rights reserved.

SLP: A zero-contact non-invasive method for pulmonary function testing

Structured Light Plethysmography (SLP) is a novel non-invasive method that uses structured light to perform pulmonary function testing that does not require physical contact with a patient. The technique produces an estimate of chest wall volume changes over time. A patient is observed continuously by two cameras and a known pattern of light (i.e. structured light) is projected onto the chest using an off-the-shelf projector. Corner features from the projected light pattern are extracted, tracked and brought into correspondence for both camera views over successive frames. A novel self calibration algorithm recovers the intrinsic and extrinsic camera parameters from these point correspondences. This information is used to reconstruct a surface approximation of the chest wall and several novel ideas for 'cleaning up' the reconstruction are used. The resulting volume and derived statistics (e.g. FVC, FEV) agree very well with data taken with a spirometer. © 2010. The copyright of this document resides with its authors.

Design, simulation and testing of large area silicon drift detectors and detector array for X-ray spectroscopy

Large area (25 mm(2)) silicon drift detectors and detector arrays (5x5) have been designed, simulated, and fabricated for X-ray spectroscopy. On the anode side, the hexagonal drift detector was designed with self-biasing spiral cathode rings (p(+)) of fixed resistance between rings and with a grounded guard anode to separate surface current from the anode current. Two designs have been used for the P-side: symmetric self-biasing spiral cathode rings (p(+)) and a uniform backside p(+) implant. Only 3 to 5 electrodes are needed to bias the detector plus an anode for signal collection. With graded electrical potential, a sub-nanoamper anode current, and a very small anode capacitance, an initial FWHM of 1.3 keV, without optimization of all parameters, has been obtained for 5.9 keV Fe-55 X-ray at RT using a uniform backside detector.

Design, simulation and testing of large area silicon drift detectors and detector array for X-ray spectroscopy

Large area (25 mm(2)) silicon drift detectors and detector arrays (5x5) have been designed, simulated, and fabricated for X-ray spectroscopy. On the anode side, the hexagonal drift detector was designed with self-biasing spiral cathode rings (p(+)) of fixed resistance between rings and with a grounded guard anode to separate surface current from the anode current. Two designs have been used for the P-side: symmetric self-biasing spiral cathode rings (p(+)) and a uniform backside p(+) implant. Only 3 to 5 electrodes are needed to bias the detector plus an anode for signal collection. With graded electrical potential, a sub-nanoamper anode current, and a very small anode capacitance, an initial FWHM of 1.3 keV, without optimization of all parameters, has been obtained for 5.9 keV Fe-55 X-ray at RT using a uniform backside detector.

Evaluation and application of micro-sampling system for inductively coupled plasma mass spectrometry

Two Meinhard microconcentric nebulizers, model AR30-07-FM02 and AR 30-07-FM005, were employed as a self-installed micro-sampling system for inductively coupled plasma-mass spectrometry (ICP-MS). The FM02 nebulizer at 22 muL/min of solution uptake rate gave the relative standard deviations of 7.6%, 3.0%, 2.7%, 1.8% for determinations (n = 10) of 20 mug/L Be, Co, In and Bi, respectively, and the detection limits (3s) of 0.14, 0.10, 0.02 and 0.01 mug/L for Be, Co In and Bi, respectively. The mass intensity of In-115 obtained by this micro-sampling system was 60% of that by conventional pneumatic nebulizer system at 1.3 mL/min. The analytical results for La, Ce, Pr and Nd in 20 muL Wistar rat amniotic fluid obtained by the present micro-sampling system were precisely in good agreement with those obtained using conventional pneumatic nebulization system.

Electrochemiluminescent microoptoprobe with mini-grid working electrode and self-contained sample container

A new electrochemiluminescence (ECL) microoptoprobe with simple structure. small sampling volume and high efficiency was developed. It was constructed by fixing the transparent gold mini-grid on the end surface of the optical fiber, and by surrounding the fiber with the counter- and reference electrodes to form a self-contained three-electrode system. The use of mini-grid electrode increased the surface area and collection efficiency. which resulted in higher ECL signal and better sensitivity. The counter electrode together with one end of the fiber formed a mini-vessel, which eliminated the need of additional container and allowed to perform ECL detection in a very small volume (about 10 mul). The microoptoprobe obtained was characterized with the Ru(bpy)(3)(2-)-tripropylamine system and was applied for the determination of oxalate and chlorpromazine (CPZ). Detection limits (S/N = 3) were 5 x 10(-7) and 1 x 10(-6) mol l(-1) for oxalate and CPZ. respectively. The linear range for oxalate and CPZ extended from 1 x 10(-6) to 1 x 10(-3) mol l(-1), and from 5 x 10(-6) to 5 x 10(-4) mol l(-1). respectively.

Self perceived health status of schizophrenic patients in Spain: an analysis of geog raphical differences using bayesian approach

Objectives. This paper explores the use of regression models for estimating health status of schizophrenic patients, from a Bayesian perspective. Our aims are: 1- To obtain a set of values of health states of the EQ-5D based on self-assessed health from a sample of schizophrenic patients. 2- To analyse the differences in the health status and in patients’ perceptions of their health status between four mental-health districts in Spain. Methods. We develop two linear models with dummy variables. The first model seeks to obtain an index of the health status of the patients using a VAS as a dependent variable and the different dimensions of EQ-5D as regressors. The second model allows to analyse the differences between the self-assessed health status in the different geographic areas and also the differences between the patients’ self-assessed health states, irrespective of their actual health state, in the different geographic areas. The analysis is done using Bayesian approach with Gibbs sampling (computer program WinBUGS 1.4). Data concerning self-assessed EQ-5D with VAS from four geographic areas of schizophrenic patients were obtained for the purposes of this analysis. Results. We obtained the health status index for this sample and analysed the differences for this index between the four geographic areas. Our study reveals variables that explain the differences in patients’ health status and differences in their health states assessment. We consider four possible scenarios.

Implementação de um sistema de rastreio do cancro do colo do útero com base na detecção do vírus HPV

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

Self-Supervised ARTMAP

Computational models of learning typically train on labeled input patterns (supervised learning), unlabeled input patterns (unsupervised learning), or a combination of the two (semisupervised learning). In each case input patterns have a fixed number of features throughout training and testing. Human and machine learning contexts present additional opportunities for expanding incomplete knowledge from formal training, via self-directed learning that incorporates features not previously experienced. This article defines a new self-supervised learning paradigm to address these richer learning contexts, introducing a neural network called self-supervised ARTMAP. Self-supervised learning integrates knowledge from a teacher (labeled patterns with some features), knowledge from the environment (unlabeled patterns with more features), and knowledge from internal model activation (self-labeled patterns). Self-supervised ARTMAP learns about novel features from unlabeled patterns without destroying partial knowledge previously acquired from labeled patterns. A category selection function bases system predictions on known features, and distributed network activation scales unlabeled learning to prediction confidence. Slow distributed learning on unlabeled patterns focuses on novel features and confident predictions, defining classification boundaries that were ambiguous in the labeled patterns. Self-supervised ARTMAP improves test accuracy on illustrative lowdimensional problems and on high-dimensional benchmarks. Model code and benchmark data are available from: http://techlab.bu.edu/SSART/.

ARTMAP: Supervised Real-Time Learning and Classification of Nonstationary Data by a Self-Organizing Neural Network

This article introduces a new neural network architecture, called ARTMAP, that autonomously learns to classify arbitrarily many, arbitrarily ordered vectors into recognition categories based on predictive success. This supervised learning system is built up from a pair of Adaptive Resonance Theory modules (ARTa and ARTb) that are capable of self-organizing stable recognition categories in response to arbitrary sequences of input patterns. During training trials, the ARTa module receives a stream {a^(p)} of input patterns, and ARTb receives a stream {b^(p)} of input patterns, where b^(p) is the correct prediction given a^(p). These ART modules are linked by an associative learning network and an internal controller that ensures autonomous system operation in real time. During test trials, the remaining patterns a^(p) are presented without b^(p), and their predictions at ARTb are compared with b^(p). Tested on a benchmark machine learning database in both on-line and off-line simulations, the ARTMAP system learns orders of magnitude more quickly, efficiently, and accurately than alternative algorithms, and achieves 100% accuracy after training on less than half the input patterns in the database. It achieves these properties by using an internal controller that conjointly maximizes predictive generalization and minimizes predictive error by linking predictive success to category size on a trial-by-trial basis, using only local operations. This computation increases the vigilance parameter ρa of ARTa by the minimal amount needed to correct a predictive error at ARTb· Parameter ρa calibrates the minimum confidence that ARTa must have in a category, or hypothesis, activated by an input a^(p) in order for ARTa to accept that category, rather than search for a better one through an automatically controlled process of hypothesis testing. Parameter ρa is compared with the degree of match between a^(p) and the top-down learned expectation, or prototype, that is read-out subsequent to activation of an ARTa category. Search occurs if the degree of match is less than ρa. ARTMAP is hereby a type of self-organizing expert system that calibrates the selectivity of its hypotheses based upon predictive success. As a result, rare but important events can be quickly and sharply distinguished even if they are similar to frequent events with different consequences. Between input trials ρa relaxes to a baseline vigilance pa When ρa is large, the system runs in a conservative mode, wherein predictions are made only if the system is confident of the outcome. Very few false-alarm errors then occur at any stage of learning, yet the system reaches asymptote with no loss of speed. Because ARTMAP learning is self stabilizing, it can continue learning one or more databases, without degrading its corpus of memories, until its full memory capacity is utilized.

Self-adaptability and man-in-the-loop: a dilemma in autonomic computing systems

This paper addresses some controversial issues relating to two main questions. Firstly, we discuss 'man-in-the loop' issues in SAACS. Some people advocate this must always be so that man's decisions can override autonomic components. In this case, the system has two subsystems - man and machine. Can we, however, have a fully autonomic machine - with no man in sight; even for short periods of time? What kinds of systems require man to always be in the loop? What is the optimum balance in self-to-human control? How do we determine the optimum? How far can we go in describing self-behaviour? How does a SAACS system handle unexpected behaviour? Secondly, what are the challenges/obstacles in testing SAACS in the context of self/human dilemma? Are there any lesson to be learned from other programmes e.g. Star-wars, aviation and space explorations? What role human factors and behavioural models play whilst in interacting with SAACS?.

Towards policy-based self-configuration of embedded systems

This paper describes work towards the deployment of flexible self-management into real-time embedded systems. A challenging project which focuses specifically on the development of a dynamic, adaptive automotive middleware is described, and the specific self-management requirements of this project are discussed. These requirements have been identified through the refinement of a wide-ranging set of use cases requiring context-sensitive behaviours. A sample of these use-cases is presented to illustrate the extent of the demands for self-management. The strategy that has been adopted to achieve self-management, based on the use of policies is presented. The embedded and real-time nature of the target system brings the constraints that dynamic adaptation capabilities must not require changes to the run-time code (except during hot update of complete binary modules), adaptation decisions must have low latency, and because the target platforms are resource-constrained the self-management mechanism have low resource requirements (especially in terms of processing and memory). Policy-based computing is thus and ideal candidate for achieving the self-management because the policy itself is loaded at run-time and can be replaced or changed in the future in the same way that a data file is loaded. Policies represent a relatively low complexity and low risk means of achieving self-management, with low run-time costs. Policies can be stored internally in ROM (such as default policies) as well as externally to the system. The architecture of a designed-for-purpose powerful yet lightweight policy library is described. A suitable evaluation platform, supporting the whole life-cycle of feasibility analysis, concept evaluation, development, rigorous testing and behavioural validation has been devised and is described.