In this work, we propose that packets travelling across a wireless sensor network (WSN) can be seen as the active agents that make up a complex system, just like a bird flock or a fish school, for instance. From this perspective, the tools and models that have been developed to study this kind of systems have been applied. This is in order to create a distributed congestion control based on a set of simple rules programmed at the nodes of the WSN. Our results show that it is possible to adapt the carried traffic to the network capacity, even under stressing conditions. Also, the network performance shows a smooth degradation when the traffic goes beyond a threshold which is settled by the proposed self-organized control. In contrast, without any control, the network collapses before this threshold. The use of the proposed solution provides an effective strategy to address some of the common problems found in WSN deployment by providing a fair packet delivery. In addition, the network congestion is mitigated using adaptive traffic mechanisms based on a satisfaction parameter assessed by each packet which has impact on the global satisfaction of the traffic carried by the WSN. In this work, we propose that packets travelling across a wireless sensor network (WSN) can be seen as the active agents that make up a complex system, just like a bird flock or a fish school, for instance. From this perspective, the tools and models that have been developed to study this kind of systems have been applied. This is in order to create a distributed congestion control based on a set of simple rules programmed at the nodes of the WSN. Our results show that it is possible to adapt the carried traffic to the network capacity, even under stressing conditions. Also, the network performance shows a smooth degradation when the traffic goes beyond a threshold which is settled by the proposed self-organized control. In contrast, without any control, the network collapses before this threshold. The use of the proposed solution provides an effective strategy to address some of the common problems found in WSN deployment by providing a fair packet delivery. In addition, the network congestion is mitigated using adaptive traffic mechanisms based on a satisfaction parameter assessed by each packet which has impact on the global satisfaction of the traffic carried by the WSN.

Scientific research often involves collaboration among researchers, and coauthorship networks are a common means of exploring these collaborations. However, traditional coauthorship networks represent coauthorship relations using simple links, i.e., pairwise interactions, which fail to capture the strength of scientific collaborations in either small or large groups. In this study, we propose a novel methodology to address this issue, which involves using a multilayer network model that captures the strength of coauthorship relations and employs a convergence index to identify the collaboration order in which these properties converge. We apply this methodology to investigate the collaborative behavior of researchers in the context of the three main public universities in Mexico over the last decade, using Scopus data as the primary source of information. Our study reveals that community structure emerges in low-order collaborations, and higher-order collaborations lead to increased clustering and centrality measures. Our methodology provides a comprehensive and insightful way of analyzing scientific collaborations and sheds light on the dynamics of scientific collaboration, providing a valuable tool for future studies. Our proposed model and convergence index can be applied to other scientific domains to better capture the strength of collaborations among researchers.

We propose a distributed rewiring model which starts with a planar graph embedded into the Euclidean space and then behaves as a distributed system, where each node is provided with a set of dynamic links. The proposed rewiring evolves through cycles, where nodes explore the network to identify possible shortcuts and rewire their dynamic links. The rewiring decisions are subject to Euclidean and geodesic distance constrains. The emerging networks were assessed through topological and robustness analyses. We found that the networks display a variety of characteristics observed in complex networks encompassing phenomena such as preferential attachment, the distinctive traits of small-world networks, the presence of community structures, and robustness against degradation process. We consider that our proposal can be applied in the design of those self-managed systems in which there is a limitation on communication resources that can be represented by the Euclidean distance and, however, the components themselves can deploy strategies to optimize the transport of information and develop tolerance before contingencies.

The microbiota-gut-brain axis suggests that the gut microbiota has a bidirectional influence on the brain and mood. In this article, we explore how the gut microbiota can positively impact our mental health. Probiotic bacteria not only help digest food but are also involved in the production of neurotransmitters such as serotonin and gamma-aminobutyric acid (GABA), key in mood regulation. Through a review of recent scientific studies, we analyze how probiotics of the Lactobacillus genus intervene in the synthesis of tryptophan, an amino acid linked to happiness, in addition to decreasing inflammation, reducing stress, and strengthening the intestinal barrier. The findings support the idea that a balanced microbiota could prevent or alleviate disorders such as anxiety or depression. Although more clinical studies are needed, probiotics are emerging as a promising alternative for caring for the mind from the gut.

We propose a distributed rewiring model which starts with a planar graph embedded into the Euclidean space and then behaves as a distributed system, where each node is provided with a set of dynamic links. The proposed rewiring evolves through cycles, where nodes explore the network to identify possible shortcuts and rewire their dynamic links. The rewiring decisions are subject to Euclidean and geodesic distance constrains. The emerging networks were assessed through topological and robustness analyses. We found that the networks display a variety of characteristics observed in complex networks encompassing phenomena such as preferential attachment, the distinctive traits of small-world networks, the presence of community structures, and robustness against degradation process. We consider that our proposal can be applied in the design of those self-managed systems in which there is a limitation on communication resources that can be represented by the Euclidean distance and, however, the components themselves can deploy strategies to optimize the transport of information and develop tolerance before contingencies.