Avalanche precursors of failure in hierarchical fuse networks

We study precursors of failure in hierarchical random fuse network models which can be considered as idealizations of hierarchical (bio)materials where fibrous assemblies are held together by multi-level (hierarchical) cross-links. When such structures are loaded towards failure, the patterns of precursory avalanche activity exhibit generic scale invariance: Irrespective of load, precursor activity is characterized by power-law avalanche size distributions without apparent cut-off, with power-law exponents that decrease continuously with increasing load. This failure behavior and the ensuing super-rough crack morphology differ significantly from the findings in non-hierarchical structures

First anatomical network analysis of fore- and hindlimb musculoskeletal modularity in bonobos, common chimpanzees, and humans

Studies of morphological integration and modularity, and of anatomical complexity in human evolution typically focus on skeletal tissues. Here we provide the first network analysis of the musculoskeletal anatomy of both the fore- and hindlimbs of the two species of chimpanzee and humans. Contra long-accepted ideas, network analysis reveals that the hindlimb displays a pattern opposite to that of the forelimb: Pan big toe is typically seen as more independently mobile, but humans are actually the ones that have a separate module exclusively related to its movements. Different fore- vs hindlimb patterns are also seen for anatomical network complexity (i.e., complexity in the arrangement of bones and muscles). For instance, the human hindlimb is as complex as that of chimpanzees but the human forelimb is less complex than in Pan. Importantly, in contrast to the analysis of morphological integration using morphometric approaches, network analyses do not support the prediction that forelimb and hindlimb are more dissimilar in species with functionally divergent limbs such as bipedal humans

The impact of contact tracing in clustered populations

The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease

The role of asymmetric interactions on the effect of habitat destruction in mutualistic networks

Plant-pollinator mutualistic networks are asymmetric in their interactions: specialist plants are pollinated by generalist animals, while generalist plants are pollinated by a broad involving specialists and generalists. It has been suggested that this asymmetric ---or disassortative--- assemblage could play an important role in determining the equal susceptibility of specialist and generalist plants under habitat destruction. At the core of the argument lies the observation that specialist plants, otherwise candidates to extinction, could cope with the disruption thanks to their interaction with generalist pollinators. We present a theoretical framework that supports this thesis. We analyze a dynamical model of a system of mutualistic plants and pollinators, subject to the destruction of their habitat. We analyze and compare two families of interaction topologies, ranging from highly assortative to highly disassortative ones, as well as real pollination networks. We found that several features observed in natural systems are predicted by the mathematical model. First, there is a tendency to increase the asymmetry of the network as a result of the extinctions. Second, an entropy measure of the differential susceptibility to extinction of specialist and generalist species show that they tend to balance when the network is disassortative. Finally, the disappearance of links in the network, as a result of extinctions, shows that specialist plants preserve more connections than the corresponding plants in an assortative system, enabling them to resist the disruption.Comment: 14 pages, 7 figure

Dynamics of multi-stage infections on networks

This paper investigates the dynamics of infectious diseases with a nonexponentially distributed infectious period. This is achieved by considering a multistage infection model on networks. Using pairwise approximation with a standard closure, a number of important characteristics of disease dynamics are derived analytically, including the final size of an epidemic and a threshold for epidemic outbreaks, and it is shown how these quantities depend on disease characteristics, as well as the number of disease stages. Stochastic simulations of dynamics on networks are performed and compared to output of pairwise models for several realistic examples of infectious diseases to illustrate the role played by the number of stages in the disease dynamics. These results show that a higher number of disease stages results in faster epidemic outbreaks with a higher peak prevalence and a larger final size of the epidemic. The agreement between the pairwise and simulation models is excellent in the cases we consider

Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure

Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics

mHealth system for the early detection of infectious diseases using biomedical signals

Latin American Congress on Automation and Robotics LACAR 2019, 30/10/2019-01/11/2019, Cali, Colombia.Detection at an early stage of an infection is a major clinical challenge. An infection that is not diagnosed in time can not only seriously affect the health of the infected patient, but also spread and initiate a contagious approach towards other people. This paper deals with mHealth system for medical care and pre-diagnosis. The developed mHealth system use an Android App that collects physiological signals from the patients with a portable and easy-to-use sensors kit. The focus of the work is put on being able to build a low-cost system that using a very small amounts of data (one set record per patient and day). The processed data are uploaded to an online database to train a clinical decision support system to automatically diagnose infections. The mHealth system may be operated by the same personnel on site not requiring to be medical or computational skilled at all. The implementation takes five kinds of measures simultaneously (Electrodermal Activity, Body Temperature, Blood Pressure, Heart Beat Rate and Oxygen Saturation (SPO2)). A real implementation has been tested and results confirm that the sampling process can be done very fast and steadily Finally, the App usability was tested, showing a fast learning curve and no significant differences are observable in learning time by people with different skills or age. These usability factors are key for the mHealth system success

Modeling the transfer of land and water from agricultural to urban uses in the Middle Rio Grande Basin, New Mexico.

Social and ecological scientists emphasize that effective natural resource management depends in part on understanding the dynamic relationship between the physical and non-physical process associated with resource consumption. In this case, the physical processes include hydrological, climatological and ecological dynamics, and the non-physical process include social, economic and cultural dynamics among humans who do the resource consumption. This project represents a case study aimed at modeling coupled social and physical processes in a single decision support system. In central New Mexico, individual land use decisions over the past five decades have resulted in the gradual transformation of the Middle Rio Grande Valley from a primarily rural agricultural landscape to a largely urban one. In the arid southwestern U.S., the aggregate impact of individual decisions about land use is uniquely important to understand, because scarce hydrological resources will likely limit the viability of resulting growth and development trajectories. This decision support tool is intended to help planners in the area look forward in their efforts to create a collectively defined 'desired' social landscape in the Middle Rio Grande. Our research question explored the ways in which socio-cultural values impact decisions regarding that landscape and associated land use. Because of the constraints hydrological resources place on land use, we first assumed that water use, as embodied in water rights, was a reasonable surrogate for land use. We thought that modeling the movement of water rights over time and across water source types (surface and ground) would provide planners with insight into the possibilities for certain types of decisions regarding social landscapes, and the impact those same decisions would have on those landscapes. We found that water rights transfer data in New Mexico is too incomplete and inaccurate to use as the basis for the model. Furthermore, because of its lack of accuracy and completeness, water rights ownership was a poor indicator of water and land usage habits and patterns. We also found that commitment among users in the Middle Rio Grande Valley is to an agricultural lifestyle, not to a community or place. This commitment is conditioned primarily by generational cohort and past experience. If conditions warrant, many would be willing to practice the lifestyle elsewhere. A related finding was that sometimes the pressure to sell was not the putative price of the land, but the taxes on the land. These taxes were, in turn, a function of the level of urbanization of the neighborhood. This urbanization impacted the quality of the agricultural lifestyle. The project also yielded some valuable lessons regarding the model development process. A facilitative and collaborative style (rather than a top-down, directive style) was most productive with the inter-disciplinary , inter-institutional team that worked on the project. This allowed for the emergence of a process model which combined small, discipline- and/or task-specific subgroups with larger, integrating team meetings. The project objective was to develop a model that could be used to run test scenarios in which we explored the potential impact of different policy options. We achieved that objective, although not with the level of success or modeling fidelity which we had hoped for. This report only describes very superficially the results of test scenarios, since more complete analysis of scenarios would require more time and effort. Our greatest obstacle in the successful completion of the project was that required data were sparse, of poor quality, or completely nonexistent. Moreover, we found no similar modeling or research efforts taking place at either the state or local level. This leads to a key finding of this project: that state and local policy decisions regarding land use, development, urbanization, and water resource allocation are being made with minimal data and without the benefit of economic or social policy analysis

Gambling disorder and suicide: An overview of the associated co-morbidity and clinical characteristics

Context: A high prevalence of suicide and attempted suicide in relation to gambling disorder is in increasing evidence in current scientific data. The objective of this review was to explore if there was a primary correlation between psychiatric co-morbidities and gambling and/or a secondary correlation with suicide acts. Evidence Acquisition: We performed a critical analysis of the most recent papers in the scientific literature in this regard and report on the most significant findings. Results: A direct relationship between gambling and suicidality was highlighted in a number of European, American, and Asian countries. However, it was not clear whether or not gambling increased the risk of suicidal behavior. Twogeneral trends were noted. The first was that gamblers with extreme gambling behavior incurred economic losses and debts to such an extent that suicidal acts appeared to be the only solution. The second was that suicidal acts by gamblers were precipitated by interpersonal and/or working challenges, in conjunction with personality traits of impulsivity and psychiatric co-morbidities. Conclusions: A combination of impulsivity, certain psychiatric disorders, and social factors may explain the frequent occurrence of suicidal behavior in gamblers