MATLAB analysis package for 'Extreme Bow Shock Motions and Magnetopause Compression During the 20 April 2002 Storm' (Cluster/ACE event analysis, figures, and Table 1
Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
MATLAB analysis package accompanying the article "Extreme Bow Shock Motions and Magnetopause Compression During the 20 April 2002 Storm: Predictability Limits at an ICME Leading Edge" (T. Y. Alrefay, submitted to Earth and Space Science). The package downloads all required spacecraft data from public archives and reproduces every figure and table of the article, end to end, from a single driver script. What it does. The pipeline (1) retrieves ACE MAG/SWEPAM data and the OMNI 1-min SYM-H index from NASA/GSFC CDAWeb via the CDAS REST service, and full-resolution Cluster FGM data from the ESA Cluster Science Archive via its HAPI server; (2) performs minimum variance analysis with bootstrap uncertainty, Rankine–Hugoniot jump analysis and discontinuity classification, and four-spacecraft constant-velocity timing of bow shock crossings; (3) implements the two-anchor arrival-time reconstruction introduced in the article, propagating the interplanetary front both from the L1 monitor and from the near-Earth four-spacecraft-timed crossing, with full error budgets; (4) evaluates the Shue et al. (1998) magnetopause and Farris & Russell (1994) bow shock standoff before and after the discontinuity; and (5) computes the deterministic bow shock velocity prediction of Meziane et al. (2015, Eq. 4) for comparison with the six timed crossings. Reproduces: Figures 1–5 and Table 1 of the article. Requirements: MATLAB (base installation; no toolboxes required) and internet access for the first run's data download. Run main_analysis.m section by section; see README for details and for the mapping between scripts, figures, and table. Data sources: all input data are publicly available from CDAWeb (https://cdaweb.gsfc.nasa.gov) and the ESA Cluster Science Archive (https://csa.esac.esa.int); no spacecraft data are redistributed in this archive.
Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 728 citations· ⚡54
Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.