Information management system: A summary discussion

An information management system is proposed for use in the space shuttle sortie, the modular space station, the tracking data relay satellite and associated ground support systems. Several different information management functions, including data acquisition, transfer, storage, processing, control and display are integrated in the system

Cluster Morphologies and Model-independent Y_(SZ) Estimates from Bolocam Sunyaev-Zel'dovich Images

We present initial results from our ongoing program to image the Sunyaev-Zel'dovich (SZ) effect in galaxy clusters at 143 GHz using Bolocam; five clusters and one blank field are described in this manuscript. The images have a resolution of 58 arcsec and a radius of ≃ 6-7 arcmin, which is approximately r_(500)-2r_(500) for these clusters. We effectively high-pass filter our data in order to subtract noise sourced by atmospheric fluctuations, but we are able to obtain unbiased images of the clusters by deconvolving the effects of this filter. The beam-smoothed rms is ≃ 10 μK_(CMB) in these images; with this sensitivity, we are able to detect the SZ signal to beyond r_(500) in binned radial profiles. We have fit our images to beta and Nagai models, fixing spherical symmetry or allowing for ellipticity in the plane of the sky, and we find that the best-fit parameter values are in general consistent with those obtained from other X-ray and SZ data. Our data show no clear preference for the Nagai model or the beta model due to the limited spatial dynamic range of our images. However, our data show a definitive preference for elliptical models over spherical models, quantified by an F ratio of ≃ 20 for the two models. The weighted mean ellipticity of the five clusters is ϵ = 0.27 ± 0.03, consistent with results from X-ray data. Additionally, we obtain model-independent estimates of Y_(500), the integrated SZ y-parameter over the cluster face to a radius of r_(500), with systematics-dominated uncertainties of ≃ 10%. Our Y_(500) values, which are free from the biases associated with model-derived Y_(500) values, scale with cluster mass in a way that is consistent with both self-similar predictions and expectations of a ≃ 10% intrinsic scatter

Development of lanthanum nickelate as a cathode for use in intermediate temperature solid oxide fuel cells

The performance of lanthanum nickelate, La2NiO4+δ (LNO), as a cathode in IT-SOFCs with the electrolyte cerium gadolinium oxide, Ce0.9Gd0.1O2−δ (CGO), has been investigated by AC impedance spectroscopy of symmetrical cells. A significant reduction in the area specific resistance (ASR) has been achieved with a layered cathode structure consisting of a thin compact LNO layer between the dense electrolyte and porous electrode. This decrease in ASR is believed to be a result of contact at the electrolyte/cathode boundary enhancing the oxygen ion transfer to the electrolyte. An ASR of 1.0 Ω cm2 at 700 °C was measured in a symmetrical cell with this layered structure, compared to an ASR of 7.4 Ω cm2 in a cell without the compact layer. In addition, further improvements were observed by enhancing the cell current collection and it is anticipated that a symmetrical cell consisting of a layered structure with adequate current collection would lower these ASR values further

Organizational culture, organizational change and emotions: a qualitative study

Change triggers emotions as employees experience the processes and outcomes of organizational transformation. An organization's affective culture, which shapes the way emotions are experienced and expressed, plays a particularly important part during changes to the culture and other aspects of organizational life. This article contributes to the literature by illustrating the relationships between culture, change and emotions and presents the results of a qualitative study. The study found that when participants' values were congruent with those of the organization, they tended to react to change more positively. Cultural change provoked emotional reactions, often of an intense nature. When emotions were acknowledged and treated with respect, people became more engaged with the change. Attitudes to existing culture also produced emotional responses to aspects of change

Investigation of Graded La2NiO4+ Cathodes to Improve SOFC Electrochemical Performance

Mixed ionic and electronic conducting MIEC oxides are promising materials for use as cathodes in solid oxide fuel cells SOFCs due to their enhanced electrocatalytic activity compared with electronic conducting oxides. In this paper, the MIEC oxide La2NiO4+ was prepared by the sol-gel route. Graded cathodes were deposited onto yttria-stabilized zirconia YSZ pellets by dip-coating, and electrochemical impedance spectroscopy studies were performed to characterize the symmetrical cell performance. By adapting the slurries, cathode layers with different porosities and thicknesses were obtained. A ceria gadolinium oxide CGO barrier layer was introduced, avoiding insulating La2Zr2O7 phase formation and thus reducing resistance polarization of the cathode. A systematic correlation between microstructure, composition, and electrochemical performance of these cathodes has been performed. An improvement of the electrochemical performance has been demonstrated, and a reduction in the area specific resistance ASR by a factor of 4.5 has been achieved with a compact interlayer of La2NiO4+ between the dense electrolyte and the porous La2NiO4+ cathode layer. The lowest observed ASR of 0.11 cm2 at 800°C was obtained from a symmetrical cell composed of a YSZ electrolyte, a CGO interlayer, an intermediate compact La2NiO4+ layer, a porous La2NiO4+ electrode layer, and a current collection layer of platinum paste

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

IDENTIFICATION OF THE POP-UP ACCELERATION WAVEFORM SIGNAL: A CASE STUDY

The purpose of this case study was to assess the feasibility of using accelerometers to identify and analyse the acceleration waveform signals of the pop-up movement. An experienced male recreational surfer performed 10 dry-land pop-ups with accelerometers (1000 Hz) attached to the sacrum and lateral aspect of each shank. The waveform signals of the pop-up were successfully identified at each instrumentation site using a combination of acceleration and video data. Several movement events that occurred during the pop-up were also able to be detected within each of the mean resultant acceleration signals. These findings suggest that accelerometers can viably be used to detect and analyse the waveform signals of the pop-up. This provides initial evidence that accelerometers are a suitable tool for collecting kinematic data of surfing performances

The discovery of lensed radio and X-ray sources behind the Frontier Fields cluster MACS J0717.5+3745 with the JVLA and Chandra

We report on high-resolution JVLA and Chandra observations of the Hubble Space Telescope (HST) Frontier Cluster MACS J0717.5+3745. MACS J0717.5+3745 offers the largest contiguous magnified area of any known cluster, making it a promising target to search for lensed radio and X-ray sources. With the high-resolution 1.0–6.5 GHz JVLA imaging in A and B configuration, we detect a total of 51 compact radio sources within the area covered by the HST imaging. Within this sample, we find seven lensed sources with amplification factors larger than two. None of these sources are identified as multiply lensed. Based on the radio luminosities, the majority of these sources are likely star-forming galaxies with star-formation rates (SFRs) of 10–50 M_☉ yr^(−1) located at 1 ≾ z ≾ 2. Two of the lensed radio sources are also detected in the Chandra image of the cluster. These two sources are likely active galactic nuclei, given their 2–10 keV X-ray luminosities of ~10^(43–44) erg s^(−1). From the derived radio luminosity function, we find evidence for an increase in the number density of radio sources at 0.6 < z < 2.0, compared to a z < 0.3 sample. Our observations indicate that deep radio imaging of lensing clusters can be used to study star-forming galaxies, with SFRs as low as ~10 M_⊙ yr^(−1), at the peak of cosmic star formation history