What country, university or research institute, performed the best on COVID-19? Bibliometric analysis of scientific literature

In this article, we conduct data mining to discover the countries, universities and companies, produced or collaborated the most research on Covid-19 since the pandemic started. We present some interesting findings, but despite analysing all available records on COVID-19 from the Web of Science Core Collection, we failed to reach any significant conclusions on how the world responded to the COVID-19 pandemic. Therefore, we increased our analysis to include all available data records on pandemics and epidemics from 1900 to 2020. We discover some interesting results on countries, universities and companies, that produced collaborated most the most in research on pandemic and epidemics. Then we compared the results with the analysing on COVID-19 data records. This has created some interesting findings that are explained and graphically visualised in the article

MASSIVE CODESIGN

This book focuses on “massive codesign”: the idea that multiple and/or numerous participants having different voices collaborate in a design pro- cess broken down into different steps and formats and resulting in a relevant and diversified amount of data. Services, strategies and scenarios are presented as the main field of ap- plication: these are complex items that demand complex processes be tac- kled, processes in which it is necessary to involve a variety of players who are largely interdependent and therefore who must collaborate in order to achieve any goal. The book essentially makes two main contributions: a “Collaborative De- sign Framework” to identify and structure codesign activities, methods and tools within massive creative processes; a “set of quick lessons learnt” to provide guidance to the conception and organisation of other massive crea- tive processes. The whole book is oriented at practice: it discusses codesign activities from the designer’s point of view, detailing issues such as process from beginning to end, activity flow, manipulability of tools, roles and rules for participants and many others. It is intended as a support for designers dealing in massive codesign processes and aims towards improved results

Information diffusion in online social networks: a simulation experiment

The advent of online social networks has completely transformed the way we communicate, with news, opinions, and ideas now spreading faster than ever before (Guille et al., 2013; Lee et al., 2022). That online social networks have a profound impact on the spread of information suggests further investigation of the relationship between network structure and information diffusion (Light & Moody, 2020). This honors thesis investigates degree assortativity – a measure of large-scale network structure that has often only been a footnote in relevant literature on infor- mation diffusion in online social networks – and its effect on the speed of informa- tion diffusion in online social networks. Two rewiring algorithms (Xulvi-Brunet & Sokolov, 2005) were applied to rewire a Facebook friend circle (n = 44) with varying degree assortativity, ranging from approximately −0.7 to 0.4. For each of the 160 rewired graphs, a random node was selected to infect (i.e., spread information to) its neighbors with probabilities ranging from 10 to 50 percent, and the number of infected nodes after each round of diffusion was recorded. Results suggest that degree assortativity and the speed of information dif- fusion have a strong inverse relationship – disassortative networks spread the same information faster. Moreover, degree assortativity appears to drive the speed of in- formation diffusion more than its correlates, clustering coefficient and average path length (Xulvi-Brunet & Sokolov, 2005)

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update

Massive Codesign

"This book focuses on ""massive codesign"": the idea that multiple and/or numerous participants having different voices collaborate in a design process broken down into different steps and formats and resulting in a relevant and diversified amount of data. Services, strategies and scenarios are presented as the main field of application: these are complex items that demand complex processes be tackled, processes in which it is necessary to involve a variety of players who are largely interdependent and therefore who must collaborate in order to achieve any goal. The book essentially makes two main contributions: a ""Collaborative Design Framework"" to identify and structure codesign activities, methods and tools within massive creative processes; a ""set of quick lessons learnt"" to provide guidance to the conception and organisation of other massive creative processes. The whole book is oriented at practice: it discusses codesign activities from the designer's point of view, detailing issues such as process from beginning to end, activity flow, manipulability of tools, roles and rules for participants and many others. It is intended as a support for designers dealing in massive codesign processes and aims towards improved results.

Accelerating the Information-Theoretic Approach of Community Detection Using Distributed and Hybrid Memory Parallel Schemes

There are several approaches for discovering communities in a network (graph). Despite being approximating in nature, discovering communities based on the laws of Information Theory has a proven standard of accuracy. The information-theoretic algorithm known as Infomap developed a decade ago for detecting communities, did not foresee the tremendous growth of social networking, multimedia, and massive information boom. To discover communities in massive networks, we have designed a distributed-memory-parallel Infomap in the MPI framework. Our design reaches scalability of over 500 processes capable of processing networks with millions of edges while maintaining quality comparable to the sequential Infomap. We have further developed a novel parallel hybrid approach for Infomap consists of both distributed and shared memory parallelism using MPI and OpenMP frameworks. This achieves a speedup of more than 11x in processing a network of over 100 million edges which is significantly greater than the state-of-the-art techniques