The SRB heat shield: Aeroelastic stability during reentry

Wind tunnel tests of a 3% scale model of the aft portion of the SRB equipped with partially scaled heat shields were conducted for the purpose of measuring fluctuating pressure levels in the aft skirt region. During these tests, the heat shields were observed to oscillate violently, the oscillations in some instances causing the heat shields to fail. High speed films taken during the tests reveal a regular pattern of waves in the fabric starting near the flow stagnation point and progressing around both sides of the annulus. The amplitude of the waves was too great, and their pattern too regular, for them to be attributed to the fluctuating pressure levels measured during the tests. The cause of the oscillations observed in the model heat shields, and whether or not similar oscillations will occur in the full scale SRB heat shield during reentry were investigated. Suggestions for modifying the heat shield so as to avoid the oscillations are provided, and recommendations are made for a program of vibration and wind tunnel tests of reduced-scale aeroelastic models of the heat shield

Sickness certification system in the United Kingdom: qualitative study of views of general practitioners in Scotland

Objectives: To explore how general practitioners operate the sickness certification system, their views on the system, and suggestions for change. Design: Qualitative focus group study consisting of 11 focus groups with 67 participants. Setting: General practitioners in practices in Glasgow, Tayside, and Highland regions, Scotland. Sample: Purposive sample of general practitioners, with further theoretical sampling of key informant general practitioners to examine emerging themes. Results: General practitioners believed that the sickness certification system failed to address complex, chronic, or doubtful cases. They seemed to develop various operational strategies for its implementation. There appeared to be important deliberate misuse of the system by general practitioners, possibly related to conflicts about roles and incongruities in the system. The doctor-patient relationship was perceived to conflict with the current role of general practitioners in sickness certification. When making decisions about certification, the general practitioners considered a wide variety of factors. They experienced contradictory demands from other system stakeholders and felt blamed for failing to make impossible reconciliations. They clearly identified the difficulties of operating the system when there was no continuity of patient care. Many wished either to relinquish their gatekeeper role or to continue only with major changes. Conclusions: Policy makers need to recognise and accommodate the range and complexity of factors that influence the behaviour of general practitioners operating as gatekeepers to the sickness certification system, before making changes. Such changes are otherwise unlikely to result in improvement. Models other than the primary care gatekeeper model should be considered

The Long Wavelength Array Software Library

The Long Wavelength Array Software Library (LSL) is a Python module that provides a collection of utilities to analyze and export data collected at the first station of the Long Wavelength Array, LWA1. Due to the nature of the data format and large-N ($\gtrsim$100 inputs) challenges faced by the LWA, currently available software packages are not suited to process the data. Using tools provided by LSL, observers can read in the raw LWA1 data, synthesize a filter bank, and apply incoherent de-dispersion to the data. The extensible nature of LSL also makes it an ideal tool for building data analysis pipelines and applying the methods to other low frequency arrays.Comment: accepted to the Journal of Astronomical Instrumentation; 24 pages, 4 figure

Modelling the adsorption-desorption behavior of CO2 in shales for permanent storage of CO2 and enhanced hydrocarbon extraction

Increasing global need for energy security has spurred a need for enhanced oil and gas recovery from unconventional reservoirs. From a carbon cycle point of view however, enhanced hydrocarbon extraction results in higher concentrations of CO 2 in the atmosphere, which is detrimental to the environment. Coupling the potential of storing CO 2 with gas and oil recovery is one approach to limit the rise in atmospheric CO 2 concentrations while allowing for subsurface hydrocarbon recovery. Over the past few years, shale gas and oil have emerged as one of the leading contributors to overall subsurface hydrocarbon recovery. In this study, we explore the potential of combining the adsorption of CO 2 with the enhanced recovery of CH 4 , and compare the results with water which is conventionally used for hydraulic fracturing. The adsorption-desorption behaviour is accounted for using published experimental Langmuir isotherm data. The model assumes a simplified fracture shape where the flow is one-dimensional and Darcy's law is obeyed. Key performance indicators include tonnes of CO 2 injected per scm CH 4 recovered, tonnes of H 2 O injected per scm CH 4 recovered and tonnes of CO 2 sequestered per tonne of CO 2 injected

Evaluating RNAlater® as a preservative for using near-infrared spectroscopy to predict Anopheles gambiae age and species.

Mosquito age and species identification is a crucial determinant of the efficacy of vector control programmes. Near-infrared spectroscopy (NIRS) has previously been applied successfully to rapidly, non-destructively, and simultaneously determine the age and species of freshly anesthetized African malaria vectors from the Anopheles gambiae s.l. species complex: An. gambiae s. s. and Anopheles arabiensis. However, this has only been achieved on freshly-collected specimens and future applications will require samples to be preserved between field collections and scanning by NIRS. In this study, a sample preservation method (RNAlater(®)) was evaluated for mosquito age and species identification by NIRS against scans of fresh samples. Two strains of An. gambiae s.s. (CDC and G3) and two strains of An. arabiensis (Dongola, KGB) were reared in the laboratory while the third strain of An. arabiensis (Ifakara) was reared in a semi-field system. All mosquitoes were scanned when fresh and rescanned after preservation in RNAlater(®) for several weeks. Age and species identification was determined using a cross-validation. The mean accuracy obtained for predicting the age of young (<7 days) or old (≥ 7 days) of all fresh (n = 633) and all preserved (n = 691) mosquito samples using the cross-validation technique was 83% and 90%, respectively. For species identification, accuracies were 82% for fresh against 80% for RNAlater(®) preserved. For both analyses, preserving mosquitoes in RNAlater(®) was associated with a highly significant reduction in the likelihood of a misclassification of mosquitoes as young or old using NIRS. Important to note is that the costs for preserving mosquito specimens with RNAlater(®) ranges from 3-13 cents per insect depending on the size of the tube used and the number of specimens pooled in one tube. RNAlater(®) can be used to preserve mosquitoes for subsequent scanning and analysis by NIRS to determine their age and species with minimal costs and with accuracy similar to that achieved from fresh insects. Cold storage availability allows samples to be stored longer than a week after field collection. Further study to develop robust calibrations applicable to other strains from diverse ecological settings is recommended

A Multi-Wavelength Study of Sgr A*: The Role of Near-IR Flares in Production of X-ray, Soft γ\gamma-ray and Sub-millimeter Emission

(abridged) We describe highlights of the results of two observing campaigns in 2004 to investigate the correlation of flare activity in Sgr A* in different wavelength regimes, using a total of nine ground and space-based telescopes. We report the detection of several new near-IR flares during the campaign based on {\it HST} observations. The level of near-IR flare activity can be as low as $\sim0.15$ mJy at 1.6 $\mu$m and continuous up to about 40% of the total observing time. Using the NICMOS instrument on the {\it HST}, the {\it XMM-Newton} and CSO observatories, we also detect simultaneous bright X-ray and near-IR flare in which we observe for the first time correlated substructures as well as simultaneous submillimeter and near-IR flaring. X-ray emission is arising from the population of near-IR-synchrotron-emitting relativistic particles which scatter submillimeter seed photons within the inner 10 Schwarzschild radii of Sgr A* up to X-ray energies. In addition, using the inverse Compton scattering picture, we explain the high energy 20-120 keV emission from the direction toward Sgr A*, and the lack of one-to-one X-ray counterparts to near-IR flares, by the variation of the magnetic field and the spectral index distributions of this population of nonthermal particles. In this picture, the evidence for the variability of submillimeter emission during a near-IR flare is produced by the low-energy component of the population of particles emitting synchrotron near-IR emission. Based on the measurements of the duration of flares in near-IR and submillimeter wavelengths, we argue that the cooling could be due to adiabatic expansion with the implication that flare activity may drive an outflow.Comment: 48 pages, 12 figures, ApJ (in press

Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier

Exploring mobile news reading interactions for news app personalisation

As news is increasingly accessed on smartphones and tablets, the need for personalising news app interactions is apparent. We report a series of three studies addressing key issues in the development of adaptive news app interfaces. We first surveyed users' news reading preferences and behaviours; analysis revealed three primary types of reader. We then implemented and deployed an Android news app that logs users' interactions with the app. We used the logs to train a classifier and showed that it is able to reliably recognise a user according to their reader type. Finally we evaluated alternative, adaptive user interfaces for each reader type. The evaluation demonstrates the differential benefit of the adaptation for different users of the news app and the feasibility of adaptive interfaces for news apps