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[en] Using two volume-limited Main galaxy samples of the Sloan Digital Sky Survey Data Release 10 (SDSS DR10), we investigate the dependence of the clustering properties of galaxies on stellar velocity dispersion by cluster analysis. It is found that in the luminous volume-limited Main galaxy sample, except at r=1.2, richer and larger systems can be more easily formed in the large stellar velocity dispersion subsample, while in the faint volume-limited Main galaxy sample, at r≥0.9, an opposite trend is observed. According to statistical analyses of the multiplicity functions, we conclude in two volume-limited Main galaxy samples: small stellar velocity dispersion galaxies preferentially form isolated galaxies, close pairs and small group, while large stellar velocity dispersion galaxies preferentially inhabit the dense groups and clusters. However, we note the difference between two volume-limited Main galaxy samples: in the faint volume-limited Main galaxy sample, at r≥0.9, the small stellar velocity dispersion subsample has a higher proportion of galaxies in superclusters (n≥200) than the large stellar velocity dispersion subsample.
[en] An emergency case contains important information and knowledge for emergency management, such as the evolution law of incidents, the vulnerability of hazard-affected carriers and the practice of emergency response. To respond effectively, we should learn from these valuable kinds of information and knowledge and utilize them. Most models of emergency cases are established based on ontology methodology. Important emergency information is semantically expressed and then analyzed by the text mining or cluster analysis methods. This type of methodology is at a disadvantage for obtaining the knowledge contained in the cases. In addition, some emergency case representation models are established using the event tree method or state chart method. However, not all the important information for emergencies can be integrated into these diagrams. The knowledge elements are not expressed with good structure, which results in a disadvantage to case-based reasoning and knowledge mining. In this paper, a comprehensive model for the representation of emergency cases is established. The proposed model combines the advantages of several conventional methods, including event tree, Bayesian conditional probability and information structured expression. Hazard-affected carrier properties, incident evolution laws and emergency response experience can be integrated and represented, which provides a good basis to employ data mining technology. With the proposed model, the general laws and successful emergency response experience contained in massive emergency cases can be obtained. Furthermore, the case-based reasoning and knowledge mining models for risk assessment, emergency preparedness and prevention, and decision-making can be developed based on effectively represented emergency cases.
[en] In this project we considered a number of variants of parallel computations of data analysis and visualization techniques for large-scale simulations and their ensembles with the goal of achieving in situ analytics.
[en] Full text: Estimates to the amount of spontaneous DNA damage sustained in mammalian cells are as high as 105 lesions per replicating cell per day. Many of these lesions are relatively easily repaired and generally well tolerated by the cell. The most toxic lesion and the greatest threat to genomic integrity is the DNA double stand break (DSB). Although researched widely, studies have focussed on long term damage induction and responses, usually at high damage levels. We have developed single molecule localisation microscopy (SMLM) assays to investigate the immediate cellular responses following low level DSB induction by camptothecin (CPT) treatment. Replication forks and nascent DNA were pulse labelled using the DNA base analogue 5-ethynyl2’-deoxyuridine (EdU) enabling direct visualisation of DNA. DNA damage response proteins were costained alongside pulsed DNA to visualise sites of DNA damage. The developed assay was then used to quantify overall replication levels at low CPT concentrations revealing complex cellular responses to damage approaching endogenous levels. (author)
[en] The Karabo GUI is a generic graphical user interface (GUI) which is currently developed at the European XFEL GmbH. It allows the complete management of the Karabo distributed control and data acquisition system. Remote applications (devices) can be instantiated, operated and terminated. Devices are listed in a live navigation view and from the self-description inherent to every device a default configuration panel is generated. The user may combine interrelated components into one project. Such a project includes persisted device configurations, custom control panels and macros. Expert panels can be built by intermixing static graphical elements with dynamic widgets connected to parameters of the distributed system. The same panel can also be used to graphically configure and execute data analysis workflows. Other features include an embedded IPython scripting console, logging, notification and alarm handling. The GUI is user-centric and will restrict display or editing capability according to the user’s role and the current device state. The GUI is based on PyQt technology and acts as a thin network client to a central Karabo GUI-Server. (author)
[en] The suggested work scenarios in radiation environment need to be iterative optimized according to the ALARA principle. Based on Virtual Reality (VR) technology and high-precision whole-body computational voxel phantom, a virtual reality-based simulation system for nuclear and radiation safety named SuperMC/RVIS has been developed for organ dose assessment and ALARA evaluation of work scenarios in radiation environment. The system architecture, ALARA evaluation strategy, advanced visualization methods and virtual reality technology used in SuperMC/RVIS are described. A case is presented to show its dose assessment and interactive simulation capabilities. (author)
[en] Hindcast skill for seasonal predictions of European climate is still very limited in current state-of-the-art prediction systems, especially for the summer season, since various different mechanisms are influencing the seasonal variability of European summer climate. Here, we focus on the first two modes of seasonal climate variability in the North-Atlantic-European sector and analyse their variability throughout the entire 20th century. With a pattern adopting cluster analysis that allows for the pattern to vary over time, we identify the North Atlantic Oscillation, a meridional pressure difference, and the East Atlantic pattern, a zonal pressure difference. We investigate their positive and negative phases in the ERA-20C reanalysis for 1900-2010 and assign which phase of either mechanism is dominating a specific summer. With this method we find that the different phases influence different regions over the North-Atlantic-European sector. We use this analysis to show in which region which domination mechanism influences hindcast skill. For this, we analyse the hindcast skill for 1900-2010 using 10 ensemble members generated by MPI-ESM-MR, starting every year in May. By identifying the different phases of the mechanisms in the individual ensemble members, we further find that the hindcast skill in the influenced regions varies strongly over time. (author)
[en] Here, we show how a readily available and free scientific visualization program—ParaView—can be used to display electric fields in interesting situations. We give a few examples and specify the individual steps that lead to highly educational representations of the fields. (paper)
[en] As the temperature and the diurnal temperature range (DTR) influence our life in many fields like agriculture and health, changes in either parameter may affect our current and future plans in such areas. This paper investigates the variability of temperature and DTR in the Levant and the Arabian Peninsula (172 stations in 12 countries) in different study periods of 10, 25, 40, 50, and 60 years. The data were tested for homogeneity using four standard tests, and we used the nonparametric statistics Mann-Kendall and Theil-Sen to calculate trend significance, direction, and magnitude in time series. Our results show a significant warming trend in 45–60% of the region with an average increase of 0.65, 0.43, and 0.30 °C/decade for the 25-, 40-, and 50-year study periods. The highest number of stations that have a significant increase in temperature occurs in spring and summer. On the other hand, decreasing DTR trends occur in about 31% of the region, mostly in the south of the Arabian Peninsula at 0.60 °C/decade. This work, additionally, provides an interactive online tool that shows the trends and temperature on zoomable maps, charts, and other visuals to station level. This tool could benefit researchers and strategic planners for the studied region. In many stations, we found that there are a significant increase in temperature and a decrease in DTR which reflect a severe change in the climate that should be considered in future planning. We recommend expanding this study to cover precipitation and other meteorological factors.
[en] Highlights: • A large group decision making approach is proposed for dependence assessment in HRA. • The assessment information is handled by interval 2-tuple linguistic variables. • The linguistic dependence assessments are clustered by a cluster analysis method. • An extended Muirhead mean operator is used to determine dependence levels. • The proposed model is illustrated by a healthcare dependence analysis case study. Human reliability analysis (HRA) is a systematic technique to assess human contribution to system risk and has been widely used in diverse complex systems. Dependence assessment among human errors is an important activity in HRA, which depends heavily on domain experts’ knowledge and experience. Normally, it is common for experts to give their judgments using linguistic labels and different types of uncertainties may exist in the dependence assessments. Additionally, the existing dependence assessment methods are limited to small-scale expert groups, which reduce the accuracy of dependence analysis with the increasing complexity of high risky systems. In this article, we develop a large group dependence assessment (LGDA) model based on interval 2-tuple linguistic variables and cluster analysis method to manage the dependence in HRA. Further, we propose an extended Muirhead mean operator to determine the dependence levels between consecutive operator actions. Finally, an empirical healthcare dependence analysis is taken as an example to illustrate the effectiveness and practicality of our proposed LGDA approach.