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📄 Numerical recipes in FORTRAN. The art of scientific computing
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Press, William H. et al. (1992) · Cambridge: University Press
Reads: 11 · Citations: 14442
DOI: N/A
🔗 https://ui.adsabs.harvard.edu/abs/1992nrfa.book.....P/abstract
📄 Astronomical Methods and Instrumentation in the Islamic World: Past, …
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MAghami Asl, Armin et al. (2025) · arXiv e-prints
Reads: 1426 · Citations: 0
DOI: 10.48550/arXiv.2511.19559
🔗 https://ui.adsabs.harvard.edu/abs/2025arXiv251119559M/abstract
#Astronomy #Astrophysics #Galaxies #HistoryAndPhilosophyOfPhysics #InstrumentationAndMethodsForAstr…
From al-Sufi's tenth-century observation of the Andromeda Galaxy as a "little cloud" to contemporary space missions, Islamic astronomy represents a millennium-spanning tradition of innovation and knowledge. This study traces its trajectory through three phases: the Golden Age (8th to 15th centuries), when scholars such as al-Biruni, al-Battani, and Ibn Sina developed instruments, cataloged the heavens, and refined theories that later influenced Copernicus; a period of decline (late 15th to 17th centuries), shaped by political fragmentation, economic shifts, and the delayed adoption of technologies such as printing and the telescope; and today's revival, marked by observatory collaborations, Olympiad successes, and emerging space programs in Morocco, Iran, Turkey, the UAE, and Saudi Arabia. This comparative analysis with Chinese and European scientific traditions shows how Islamic astronomy provided a vital link in the global history of science, transmitting mathematical rigor, observational methods, and Arabic star names that are still used today. The contemporary resurgence signals the potential for renewed contributions to astrophysics, provided that it is supported by regional observatory networks, space-based research initiatives, and educational frameworks that integrate historical heritage with modern computational science.
📄 pyclustering 0.8.1
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Novikov, Andrei et al. (2018) · Zenodo
Reads: 0 · Citations: 1
DOI: 10.5281/zenodo.1254845
🔗 https://ui.adsabs.harvard.edu/abs/2018zndo...1254845N/abstract
#Astronomy #Astrophysics #AstroAI #ClusteringDataminingClusteranalysisAiMachinelearningOscillatoryn…
pyclustering 0.8.1 library is collection of clustering algorithms, oscillatory networks, neural networks, etc. GENERAL CHANGES: Implemented feature to use specific metric for distance calculation in K-Means algorithm (pyclustering.cluster.kmeans, ccore.clst.kmeans). See: https://github.com/annoviko/pyclustering/issues/434 Implemented BANG-clustering algorithm with result visualizer (pyclustering.cluster.bang). See: https://github.com/annoviko/pyclustering/issues/424 Implemented feature to use specific metric for distance calculation in K-Medians algorithm (pyclustering.cluster.kmedians, ccore.clst.kmedians). See: https://github.com/annoviko/pyclustering/issues/429 Supported new type of input data for K-Medoids - distance matrix (pyclustering.cluster.kmedoids, ccore.clst.kmedoids). See: https://github.com/annoviko/pyclustering/issues/418 Implemented TTSAS algorithm (pyclustering.cluster.ttsas, ccore.clst.ttsas). See: https://github.com/annoviko/pyclustering/issues/398 Implemented MBSAS algorithm (pyclustering.cluster.mbsas, ccore.clst.mbsas). See: https://github.com/annoviko/pyclustering/issues/398 Implemented BSAS algorithm (pyclustering.cluster.bsas, ccore.clst.bsas). See: https://github.com/annoviko/pyclustering/issues/398 Implemented feature to use specific metric for distance calculation in K-Medoids algorithm (pyclustering.cluster.kmedoids, ccore.clst.kmedoids). See: https://github.com/annoviko/pyclustering/issues/417 Implemented distance metric collection (pyclustering.utils.metric, ccore.utils.metric). See: no reference. Supported new type of input data for OPTICS - distance matrix (pyclustering.cluster.optics, ccore.clst.optics). See: https://github.com/annoviko/pyclustering/issues/412 Supported new type of input data for DBSCAN - distance matrix (pyclustering.cluster.dbscan, ccore.clst.dbscan). See: no reference. Implemented K-Means observer and visualizer to visualize and animate clustering results (pyclustering.cluster.kmeans, ccore.clst.kmeans). See: no reference. CORRECTED MAJOR BUGS: Bug with out of range in K-Medians (pyclustering.cluster.kmedians, ccore.clst.kmedians). See: https://github.com/annoviko/pyclustering/issues/428 Bug with fast linking in PCNN (python implementation only) that wasn't used despite the corresponding option (pyclustering.nnet.pcnn). See: https://github.com/annoviko/pyclustering/issues/419
📄 MFMMerger
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Frankland, John et al. (2017) · GANIL/CNRS
Reads: 0 · Citations: 1
DOI: 10.5281/zenodo.13374037
🔗 https://ui.adsabs.harvard.edu/abs/2017zndo..13374037F/abstract
#Astronomy #Astrophysics #Software #DataProcessing #Timestamp
📄 Assessment of meso-micro offline coupling methodology based on drivin…
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Sanz Rodrigo, Javier et al. (2017) · Zenodo
Reads: 0 · Citations: 1
DOI: 10.5281/zenodo.834355
🔗 https://ui.adsabs.harvard.edu/abs/2017zndo....834355S/abstract
In this repository you can find the jupyter notebook that was used to post-process CFDWindSCM simulations of the GABLS3 diurnal cycle case. Based on this work a Windbench benchmark for wind energy applications was designed: http://windbench.net/gabls-3 The input data can be fetched from the EUDAT repository:http://doi.org/10.23728/b2share.22e419b663cb4ffca8107391b6716c1b and the validation data from the original GABLS3 website at KNMI:http://projects.knmi.nl/gabls/gabls3_scm_cabauw_obs_v33.nc The results were published in the following journal paper: Sanz Rodrigo J, Churchfield M, Kosović B (2017) A methodology for the design and testing of atmospheric boundary layer models for wind energy applications. Wind Energ. Sci. 2: 1-20, https://doi.org/10.5194/wes-2-35-2017