An opportunity cost approach for job assignment in a scalablecomputing cluster

A new method is presented for job assignment to and reassignment between machines in a computing cluster. Our method is based on a theoretical framework that has been experimentally tested and shown to be useful in practice. This “opportunity cost” method converts the usage of several heterogeneous resources in a machine to a single homogeneous “cost.” Assignment and reassignment are then performed based on that cost. This is in contrast to traditional, ad hoc methods for job assignment and reassignment. These treated each resource as an independent entity with its own constraints, as there was no clean way to balance one resource against another. Our method has been tested by simulations, as well as real executions, and was found to perform well     

Two-Photon Polymerized Shape Memory Microfibers: A New Mechanical Characterization Method in Liquid

Two-photon polymerization (TPP) is widely used to create 3D micro- and nanoscale scaffolds for biological and mechanobiological studies, which often require the mechanical characterization of the TPP fabricated structures. To satisfy physiological requirements, most of the mechanical characterizations need to be conducted in liquid. However, previous characterizations of TPP fabricated structures are all conducted in air due to the limitation of conventional micro- and nanoscale mechanical testing methods. In this study, a new experimental method is reported for testing the mechanical properties of TPP-printed microfibers in liquid. The experiments show that the mechanical behaviors of the microfibers tested in liquid are significantly different from those tested in air. By controlling the TPP writing parameters, the mechanical properties of the microfibers can be tailored over a wide range to meet a variety of mechanobiology applications. In addition, it is found that, in water, the plasticly deformed microfibers can return to their predeformed shape after tensile strain is released. The shape recovery time is dependent on the size of microfibers. The experimental method represents a significant advancement in mechanical testing of TPP fabricated structures and may help release the full potential of TPP fabricated 3D tissue scaffolds for mechanobiological studies.     

APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder

Neural Computing and Applications - In recent years, the world has dramatically moved toward using the internet of things (IoT), and the IoT has become a hot research field. Among various aspects...     

Representing situation awareness in collaborative systems: a case study in the energy distribution domain

The concept of distributed situation awareness (DSA) is currently receiving increasing attention from the human factors community. This article investigates DSA in a collaborative real-world industrial setting by discussing the results derived from a recent naturalistic study undertaken within the UK energy distribution domain. The results describe the DSA-related information used by the networks of agents involved in the scenarios analysed, the sharing of this information between the agents and the salience of different information elements used. Thus, the structure, quality and content of each network's DSA is discussed, along with the implications for DSA theory. The findings reinforce the notion that when viewing situation awareness (SA) in collaborative systems, it is useful to focus on the coordinated behaviour of the system itself, rather than on the individual as the unit of analysis and suggest that the findings from such assessments can potentially be used to inform system, procedure and training design. SA is a critical commodity for teams working in industrial systems and systems, procedures and training programmes should be designed to facilitate efficient system SA acquisition and maintenance. This article presents approaches for describing and understanding SA during real-world collaborative tasks, the outputs from which can potentially be used to inform system, training programmes and procedure design.     

Application of Self Organizing Map and SRTM data to characterize yardangs in the Lut desert,Iran

Yardangs, an exclusive landform due to intensive wind erosion, cover a large area in the hyper-arid Lut desert of Iran. This paper presents a new approach using Self Organizing Map (SOM) as unsupervised algorithm of artificial neural networks for analysis and characterization of yardangs.Nowadays, the Shuttle Radar Topography Mission (SRTM) with 3 arc sec data (approximately 90 m resolution) and nearly world wide coverage provides uniform good quality data.The SRTM 3 arc sec data were re-projected to a 90 m UTM grid. Bivariate quadratic surfaces with moving window size of 5 × 5 were fitted to this DEM. The first derivative, slope steepness and the second derivatives minimum, maximum curvature and cross-sectional curvatures were calculated as geomorphometric parameters used as input to the SOMs. 42 SOMs with different learning parameter settings, e.g. initial and final radius, number of iterations, and the effect of random initial weights on average quantization error were investigated. A SOM with a low average quantization error (0.1040) was used for further analysis. Feature space analysis, morphometric signatures, three-dimensional inspection, auxiliary data like Landsat ETM+ and high resolution satellite imagery from QuickBird facilitated the assignment of semantic meaning to the output classes in terms of geomorphometric features. Results are provided in a geographic information system as thematic maps of landform entities based on form and slope, e.g. yardangs (ridge), corridors (valley) or planar areas.The results showed that all yardangs and corridors were clearly recognized and classified by this method when their width was larger than the DEM resolution but became unrecognizable if their width is much smaller than the grid resolution. The identified yardangs and corridors are aligned NNW–SSE parallel to the prevailing direction of the strong local 120 days wind and cover about 31% and 42% of the study area respectively. The results demonstrate that SOM is a very efficient tool for analyzing aeolian landforms in hyper-arid environments that provides very useful information for terrain feature analysis in remote regions.     

Computerized screening of children congenital heart diseases

In this paper, we propose a method for automated screening of congenital heart diseases in children through heart sound analysis techniques. Our method relies on categorizing the pathological murmurs based on the heart sections initiating them. We show that these pathelogical murmur categories can be identified by examining the heart sound energy over specific frequency bands, which we call, Arash-Bands. To specify the Arash-Band for a category, we evaluate the energy of the heart sound over all possible frequency bands. The Arash-Band is the frequency band that provides the lowest error in clustering the instances of that category against the normal ones. The energy content of the Arash-Bands for different categories constitue a feature vector that is suitable for classification using a neural network. In order to train, and to evaluate the performance of the proposed method, we use a training data-bank, as well as a test data-bank, collectively consisting of ninety samples (normal and abnormal). Our results show that in more than 94% of cases, our method correctly identifies children with congenital heart diseases. This percentage improves to 100%, when we use the Jack-Knife validation method over all the 90 samples.     

Effect of SRTM resolution on morphometric feature identification using neural network—self organizing map

In this study, we present a semi-automatic procedure using Neural Networks—Self Organizing Map—and Shuttle Radar Topography Mission DEMs to characterize morphometric features of the landscape in the Man and Biosphere Reserve “Eastern Carpathians”. We investigate specially the effect of two resolutions, SIR-C with 3 arc seconds and X-SAR with 1 arc second for morphometric feature identification. Specifically we investigate how the SRTM/C band data with 30 m interpolated grid, corresponding to SRTM/X band 30 m, affect the morphometric characterization and topography derivatives. To reduce misregistration between the DEMs, spatial co-registration was performed and a RMSE of 0.48 pixel was achieved. Morphometric parameters such as slope, maximum curvature, minimum curvature and cross-sectional curvature are derived using a bivariate quadratic approximation on 90 m, 30 m and interpolated 30 m DEMs. Self Organizing Map (SOM) is used for the classification of morphometric parameters into ten exclusive and exhaustive classes. These classes were analyzed as morphometric features such as ridge, channel, crest line and planar for all data sets based on feature space (scatter plot), morphometric signatures and 3D inspection of the area. The map quality is analyzed by oblique views with contour lines overlaid. Using the X band DEM with 30 m grid as benchmark, a change detection technique was used to quantify differences in morphometric features and to assess the scale effect going from a 90 m (C-band) DEM to an interpolated 30 m DEM. The same procedure is used to study the effect of different resolutions on morphometric features. Morphometric parameters were computed by a moving window size 5 × 5 (corresponding to 450 m on the ground) over SRTM- 90 m. To cover the same ground area, a moving window size of 15 × 15 is used for the 30 m DEM. The change analysis showed the amount of resolution dependency of morphometric features. Overall, the results showed that the introduced method is very useful for identification of morphometric features based on SRTM resolution. Decreasing the grid size from 90 m to 30 m reveals considerably more detailed information emphasizing local conditions. Comparison between results from DEM-30 m as reference data set and interpolated 30 m, showed a rate of change of 31.5% which is negligible. About 17% of this rate correspond to classes with mean slope > 10°. Of the morphometric parameters, the cross sectional curvature is most sensitive to DEM resolution. Increasing spatial resolution reduces the main constrains for morphometric analysis with SRTM 90 m data, such as unrealistic features and isolated single elements in the output map. So in case of lack of high resolution data, the SRTM 90 m data could be interpolated and used for further geomorphic analysis.     

Compressible flows through micro-channels with sharp edged turns and bifurcations

Bends and bifurcations connecting straight channels are common features in micro-channel networks. We apply our recently extended Hele-Shaw asymptotic scheme to study the viscous compressible gas flow through a shallow bend (whose depth is small relative to all other characteristic dimensions). By the use of analytic-function theory, we obtain closed-form solutions for arbitrary values of the turn angle and the ratio between the upstream- and downstream- channel widths, respectively. The viscous resistance is thereby evaluated in terms of the geometrical parameters characterizing the bend. The linearity of the problem in terms of an appropriately defined quadratic form of the pressure allows us to briefly consider the role of bifurcations in the viscous resistance of tree-like micro-channel networks.     

Challenges of translating Rasmussen’s Accimap into a usable,sustainable, and useful incident reporting system: end-user attitudes following 12-month implementation

Cognition, Technology & Work - There have been several calls within the accident analysis literature to translate systems thinking-based theories and accident analysis methods into practical...