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Gustavo Carreón

Bio: Gustavo Carreón is an academic researcher from National Autonomous University of Mexico. The author has contributed to research in topics: Microblogging & Stakeholder. The author has an hindex of 2, co-authored 5 publications receiving 19 citations.

Papers
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Journal ArticleDOI
29 Dec 2017-PLOS ONE
TL;DR: A computer simulation of metro systems fed with real data from the Mexico City Metro shows that the self-organizing method improves the performance over the current one as it adapts to environmental changes at the timescale they occur, and provides recommendations to improve public transportation systems.
Abstract: The equal headway instability—the fact that a configuration with regular time intervals between vehicles tends to be volatile—is a common regulation problem in public transportation systems. An unsatisfactory regulation results in low efficiency and possible collapses of the service. Computational simulations have shown that self-organizing methods can regulate the headway adaptively beyond the theoretical optimum. In this work, we develop a computer simulation for metro systems fed with real data from the Mexico City Metro to test the current regulatory method with a novel self-organizing approach. The current model considers overall system’s data such as minimum and maximum waiting times at stations, while the self-organizing method regulates the headway in a decentralized manner using local information such as the passenger’s inflow and the positions of neighboring trains. The simulation shows that the self-organizing method improves the performance over the current one as it adapts to environmental changes at the timescale they occur. The correlation between the simulation of the current model and empirical observations carried out in the Mexico City Metro provides a base to calculate the expected performance of the self-organizing method in case it is implemented in the real system. We also performed a pilot study at the Balderas station to regulate the alighting and boarding of passengers through guide signs on platforms. The analysis of empirical data shows a delay reduction of the waiting time of trains at stations. Finally, we provide recommendations to improve public transportation systems.

13 citations

Journal ArticleDOI
01 Jan 2018
TL;DR: It is shown that emergent topics related to popular science are important because they could help to explore how science becomes of public interest and offer some important insights for studying the type of scientific content that users are more likely to tweet.
Abstract: Twitter is perhaps the most influential microblogging service, with 271 million regular users producing approximately 500 million tweets per day. Previous studies of tweets discussing scientific topics are limited to local surveys or may not be representative geographically. This indicates a need to harvest and analyse tweets with the aim of understanding the level of dissemination of science related topics worldwide. In this study, we use Twitter as case of study and explore the hypothesis of science popularization via the social stream. We present and discuss tweets related to popular science around the world using eleven keywords. We analyze a sample of 306,163 tweets posted by 91,557 users with the aim of identifying tweets and those categories formed around temporally similar topics. We systematically examined the data to track and analyze the online activity around users tweeting about popular science. In addition, we identify locations of high Twitter activity that occur in several places around the world. We also examine which sources (mobile devices, apps, and other social networks) are used to share popular science related links. Furthermore, this study provides insights into the geographic density of popular science tweets worldwide. We show that emergent topics related to popular science are important because they could help to explore how science becomes of public interest. The study also offers some important insights for studying the type of scientific content that users are more likely to tweet.

7 citations

Posted Content
TL;DR: In this paper, the authors explored the integration of different computational techniques consisting of text mining and network analysis procedures as tools for the characterization of actors in the context of environmental resource management and governance.
Abstract: Network theory is a widely used approach for characterizing the interaction between the components of complex systems. However, dealing with social, cultural and socio-ecological complex systems is challenging in their own particular ways. Interactions in such systems cannot be characterized entirely as socio-ecological because they are also culturally charged and semantically loaded from the perspective of their components. In order to achieve a better understanding, the component’s view and symbolic interpretation cannot be ignored. Due to these singular properties, we believe that for the analysis, management and decision-making regarding these kind of systems, complementary and alternative analysis rooted in text mining is required. In such a way, all stakeholder’s discourses are the base to capture additional information about the context-related properties of the interactions under study. This paper explores the integration of different computational techniques consisting of text mining and network analysis procedures as tools for the characterization of actors in the context of environmental resource management and governance. We develop a comprehensive methodology for describing the topology of the perceived interactions among stakeholders, characterizing them with the content of their discourses about moorland conservation. On one hand, the methodological approach permitted us to obtain: (i) the networks or “ego-centered networks” based on the stakeholders or actors interviews, (ii) a complete network by merging ego-centered networks, (iii) synergistic/antagonistic perceptions towards actors were represented in weighted networks, and (iv) the identification of leaders among the interviewees. On the other, text mining also permitted the analysis of interviews performed with actors, providing social and semantic context about the moorland conservation discourses. We finally integrated both approaches in a single framework. We believe that in using such methods, recommendations for self-governance may be possible. The results show the social structure of the moorland’s leaders, their cultural context and their perceptions about the importance of the moorland ecosystem. Based on these findings, decision and policy-making for institutional and stakeholder actors could be improved. Moreover, we would like to suggest that in having the right description of the system, a guided self-organization approach could be applied.

2 citations

Proceedings ArticleDOI
31 Mar 2005
TL;DR: It is shown that traditional and well‐known results from the theory of nonlinear dynamics can provide a useful ground to achieve whole genome phylogenies treating DNA as a discrete sequence and then feeding it to a dynamical system.
Abstract: One of the most important aims in evolutionary biology is the search of historical as well as structural relationships among species. In this report, we show that traditional and well‐known results from the theory of nonlinear dynamics can provide a useful ground to achieve this end. In particular, we propose whole genome phylogenies treating DNA as a discrete sequence and then feeding it to a dynamical system.

2 citations

Posted Content
TL;DR: This study critically examines the role of animal searching behavior applied to random walk models using stochastic rules and kinesis or taxis and concludes with a discussion concerning the usefulness of using optimal foraging strategies as a reliable methodology.
Abstract: In this paper, we conduct a literature review of laws of motion based on stochastic search strategies which are mainly focused on exploring highly dynamic environments. In this regard, stochastic search strategies represent an interesting alternative to cope with uncertainty and reduced perceptual capabilities. This study aims to present an introductory overview of research in terms of directional rules and searching methods mainly based on bio-inspired approaches. This study critically examines the role of animal searching behavior applied to random walk models using stochastic rules and kinesis or taxis. The aim of this study is to examine existing techniques and to select relevant work on random walks and analyze their actual contributions. In this regard, we cover a wide range of displacement events with an orientation mechanism given by a reactive behavior or a source-seeking behavior. Finally, we conclude with a discussion concerning the usefulness of using optimal foraging strategies as a reliable methodology.

1 citations


Cited by
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01 Jan 2016
TL;DR: Swarm intelligence from natural to artificial systems, where people have search hundreds of times for their chosen books, but end up in malicious downloads instead of reading a good book with a cup of coffee in the afternoon.
Abstract: Thank you very much for reading swarm intelligence from natural to artificial systems. As you may know, people have search hundreds times for their chosen books like this swarm intelligence from natural to artificial systems, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious bugs inside their computer.

189 citations

Journal ArticleDOI
03 Apr 2020
TL;DR: Different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design are summarized and identified.
Abstract: Self-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention real-world examples of systems where self-organization has managed to provide solutions that outperform classical approaches, in particular related to urban mobility. Finally, I identify when a centralized, distributed, or self-organizing control is more appropriate.

18 citations

Journal ArticleDOI
TL;DR: Self-organization can be defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components.
Abstract: Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

17 citations

Posted Content
TL;DR: The fundamental aspects of self-organization are discussed and the main usages within three primary ALife domains are listed, namely “soft” (mathematical/computational modeling), “hard’ (physical robots), and “wet”/chemical/biological systems) ALife.
Abstract: Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

15 citations

Journal ArticleDOI
TL;DR: The Google Books $N-grams dataset is used to analyze the temporal evolution of word usage in several languages, and results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language.
Abstract: The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books $N$-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of $N$-grams in a given rank, the probability that an $N$-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that $N$-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.

14 citations