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Copyright of Journal of Information Processing & Management / Joho Kanri is the property of Japan Science & Technology Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: Advances in IT infrastructure have enabled the generation and storage of very large data sets describing complex systems continuously in time. These can derive from both simulations and measurements. Analysis of such data requires the availability of scalable algorithms. In this contribution, we propose a scalable algorithm that partitions instantaneous observations (snapshots) of a complex system into kinetically distinct sets (termed basins). To do so, we use a combination of ordering snapshots employing the method’s only essential parameter, i.e., a definition of pairwise distance, and annotating the resultant sequence, the so-called progress index, in different ways. Specifically, we propose a combination of cut-based and structural annotations with the former responsible for the kinetic grouping and the latter for diagnostics and interpretation. The method is applied to an illustrative test case, and the scaling of an approximate version is demonstrated to be with being the number of snapshots. Two real-world data sets from river hydrology measurements and protein folding simulations are then used to highlight the utility of the method in finding basins for complex systems. Both limitations and benefits of the approach are discussed along with routes for future research. [Copyright &y& Elsevier]

This article explores the employment career trajectories of Master's level graduates during university and after graduation. The data consisted of all graduates who according to Finnish Centre for Pensions registers had completed a degree before age 31 in 2005, 2006 and 2007. Employment was followed using data on number of months in employment and earnings received. Trajectory analyses were used to form four different employment and earnings paths, which were named as 'traditional', 'early-entry', 'increasing' and 'ading'. Working while at university was very common: some half of the university students moved onto an early employment path where they worked virtually full-time. However this path did not necessarily guarantee easier employment after graduation. Employment prospects were more or less the same for graduates following the strategies of increasing and traditional labour market attachment, which involved less employment especially in the early stages of university. Field of study did not systematically explain different combinations of employment and studies in these three groups who secured good employment. The analysis also revealed a group comprising some 7% of the graduates whose employment career after graduation was difficult to trace based on the register data. In this heterogeneous group large numbers moved abroad and had missing data, and a larger number than average were unemployed. The results of our analyses did not lend unambiguous support to the argument that the motivation and justification for students to work while in university is to ensure good employability after graduation. Future research should address such questions as how the entry of university students into the labour market impacts the operation of the labour market, dropping out of university, and universities' practices. [ABSTRACT FROM AUTHOR]

Abstract: Eucb is a standalone program for geometrical analysis of molecular dynamics trajectories of protein systems. The program is written in GNU C++ and it can be installed in any operating system running a C++ compiler. The program performs its analytical tasks based on user supplied keywords. The source code is freely available from http://stavrakoudis.econ.uoi.gr/eucb under LGPL 3 license. Program summary: Program title: Eucb Catalogue identifier: AEIC_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEIC_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 31 169 No. of bytes in distributed program, including test data, etc.: 297 364 Distribution format: tar.gz Programming language: GNU C++ Computer: The tool is designed and tested on GNU/Linux systems Operating system: Unix/Linux systems RAM: 2 MB Supplementary material: Sample data files are available Classification: 3 Nature of problem: Analysis of molecular dynamics trajectories. Solution method: The program finds all possible interactions according to input files and the user instructions. Then it reads all the trajectory frames and finds those frames in which these interactions occur, under certain geometrical criteria. This is a blind search, without a priori knowledge if a certain interaction occurs or not. The program exports time series of these quantities (distance, angles, etc.) and appropriate descriptive statistics. Running time: Depends on the input data and the required options. [ABSTRACT FROM AUTHOR]

Advances in biomolecular simulation methods and access to large scale computer resources have led to a massive increase in the amount of data generated. The key enablers have been optimization and parallelization of the simulation codes. However, much of the software used to analyze trajectory data from these simulations is still run in serial, or in some cases many threads via shared memory. Here, we describe the addition of multiple levels of parallel trajectory processing to the molecular dynamics simulation analysis software CPPTRAJ. In addition to the existing OpenMP shared-memory parallelism, CPPTRAJ now has two additional levels of message passing (MPI) parallelism involving both across-trajectory processing and across-ensemble processing. All three levels of parallelism can be simultaneously active, leading to significant speed ups in data analysis of large datasets on the NCSA Blue Waters supercomputer by better leveraging the many available nodes and its parallel file system. © 2018 Wiley Periodicals, Inc., (© 2018 Wiley Periodicals, Inc.)