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Computationally Intensive Problems in General Math and Engineering: Applicative Research and Education

This two-part special issue for the Journal of Big Data covers computationally intensive problems in engineering and focuses on mathematical mechanisms of interest for emerging problems such as Partial Difference Equations, Tensor Calculus, Mathematical Logic, and Algorithmic Enhancements based on Artificial Intelligence. Applications of the research highlighted in the collection include, but are not limited to: Earthquake Engineering, Spatial Data Analysis, Geo Computation, Geophysics, Genomics and Simulations for Nature Based Construction, and Aerospace Engineering. Featured lead articles are co-authored by three esteemed Nobel laureates: Jean-Marie Lehn, Konstantin Novoselov, and Dan Shechtman.

This collection is part of a larger special issue covering general problems in math and engineering. You can view part one of this special issue here.

New Content ItemProf. Veljko Milutinovic
Fellow of the IEEE and of the Academy of Europe, Indiana University, Bloomington, IND, USA, Adjunct Professor, University of Belgrade, SRB, EUR, Visiting Professor

Prof. Veljko Milutinovic (1951) received his PhD from the University of Belgrade in Serbia, spent about a decade on various faculty positions in the USA (mostly at Purdue University and more recently at the University of Indiana in Bloomington), and was a co-designer of the DARPAs pioneering GaAs RISC microprocessor on 200MHz (about a decade before the first commercial effort on that same speed) and was a co-designer also of the related GaAs Systolic Array (with 4096 GaAs microprocessors). Later, for almost three decades, he taught and conducted research at the University of Belgrade in Serbia, for departments of EE, MATH, BA, and PHYS/CHEM. His research is mostly in datamining algorithms and dataflow computing, with the emphasis on mapping of data analytics algorithms onto fast energy efficient architectures. Most of his research was done in cooperation with industry (Intel, Fairchild, Honeywell, Maxeler, HP, IBM, NCR, RCA, etc... ). For 10 of his books, forewords were written by 10 different Nobel Laureates with whom he cooperated on his past industry sponsored projects. He published 40 books (mostly in the USA), he has over 100 papers in SCI journals (mostly in IEEE and ACM journals), and he presented invited talks at over 400 destinations worldwide. He has well over 1000 Thomson-Reuters WoS citations, well over 1000 Elsevier SCOPUS citations, and about 4000 Google Scholar citations. His Google Scholar h index is equal to 36. He is a Life Fellow of the IEEE since 2003 and a Member of The Academy of Europe since 2011. He is a member of the Serbian National Academy of Engineering and a Foreign Member of the Montenegro National Academy of Sciences and Arts.

New Content ItemJean-Marie Lehn
University of Strasbourg, France
Jean-Marie Lehn received the Nobel Prize in Chemistry in 1987, together with Donald Cram and Charles Pedersen for his synthesis of cryptands. Lehn was an early innovator in the field of supermolecular chemistry, i.e. the chemistry of host-guest molecular assemblies created by intermolecular interactions, and continues to innovate in this field.

New Content ItemKonstantin Novoselov
University of Manchester, UK
Konstantin Novoselov received the Nobel Prize in Physics in 2010, together with Andre Geim, for their groundbreaking experiments on graphene, a two-dimensional material with remarkable properties such as high electrical conductivity, mechanical strength, and transparency. Novoselov and Geim's discovery of graphene was published in 2004, and it opened up new possibilities for the development of innovative technologies in various fields.

New Content ItemDan Shechtman
Technion, Israel
Dan Shechtman received the Nobel Prize in Chemistry in 2011, for the discovery of quasicrystals, a type of solid material with a highly ordered structure that was previously thought to be impossible. However, further investigation revealed that the pattern was due to a new type of crystal structure, which did not fit the conventional rules of crystallography. This discovery eventually led to a new field of research in materials science.

  1. Processing large-scale graphs is challenging due to the nature of the computation that causes irregular memory access patterns. Managing such irregular accesses may cause significant performance degradation on...

    Authors: Amin Sahebi, Marco Barbone, Marco Procaccini, Wayne Luk, Georgi Gaydadjiev and Roberto Giorgi
    Citation: Journal of Big Data 2023 10:95
  2. This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, ...

    Authors: Zoran Babović, Branislav Bajat, Dusan Barac, Vesna Bengin, Vladan Đokić, Filip Đorđević, Dražen Drašković, Nenad Filipović, Stephan French, Borko Furht, Marija Ilić, Ayhan Irfanoglu, Aleksandar Kartelj, Milan Kilibarda, Gerhard Klimeck, Nenad Korolija…
    Citation: Journal of Big Data 2023 10:89
  3. Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical quantities. Various sensors, measuring different parameters, c...

    Authors: Matevž Hribernik, Sašo Tomažič, Anton Umek and Anton Kos
    Citation: Journal of Big Data 2023 10:68
  4. The number of published scientific paper grows rapidly each year, totaling more than 2.9 million annually. New methodologies and systems have been developed to analyze scientific production and performance ind...

    Authors: Irena Mitrović, Marko Mišić and Jelica Protić
    Citation: Journal of Big Data 2023 10:64
  5. This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia)....

    Authors: Ognjen Antonijević, Slobodan Jelić, Branislav Bajat and Milan Kilibarda
    Citation: Journal of Big Data 2023 10:54

Annual Journal Metrics

  • Citation Impact 2023
    Journal Impact Factor: 8.6
    5-year Journal Impact Factor: 12.4
    Source Normalized Impact per Paper (SNIP): 3.853
    SCImago Journal Rank (SJR): 2.068

    Speed 2023
    Submission to first editorial decision (median days): 56
    Submission to acceptance (median days): 205

    Usage 2023
    Downloads: 2,559,548
    Altmetric mentions: 280