Understanding the food-energy-water nexus is necessary to identify risks and inform strategies for nexus governance to support resilient, secure, and sustainable societies. To manage risks and realize efficiencies, we must understand not only how these systems are physically connected but also how they are institutionally linked. It is important to understand how actors who make planning, management, and policy decisions understand the relationships among components of the systems. Our question is: How do stakeholders involved in food, energy, and water governance in Phoenix, Arizona understand the nexus and what are the implications for integrated nexus governance? We employ a case study design, generate qualitative data through focus groups and interviews, and conduct a content analysis. While stakeholders in the Phoenix area who are actively engaged in food, energy, and water systems governance appreciate the rationale for nexus thinking, they recognize practical limitations to implementing these concepts. Concept maps of nexus interactions provide one view of system interconnections that be used to complement other ways of knowing the nexus, such as physical infrastructure system diagrams or actor-networks. Stakeholders believe nexus governance could be improved through awareness and education, consensus and collaboration, transparency, economic incentives, working across scales, and incremental reforms.

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

We investigate the emergence of extreme events in interdependent networks. We introduce an inter-layer traffic resource competing mechanism to account for the limited capacity associated with distinct network layers. A striking finding is that, when the number of network layers and/or the overlap among the layers are increased, extreme events can emerge in a cascading manner on a global scale. Asymptotically, there are two stable absorption states: a state free of extreme events and a state of full of extreme events, and the transition between them is abrupt. Our results indicate that internal interactions in the multiplex system can yield qualitatively distinct phenomena associated with extreme events that do not occur for independent network layers. An implication is that, e.g., public resource competitions among different service providers can lead to a higher resource requirement than naively expected. We derive an analytical theory to understand the emergence of global-scale extreme events based on the concept of effective betweenness. We also articulate a cost-effective control scheme through increasing the capacity of very few hubs to suppress the cascading process of extreme events so as to protect the entire multi-layer infrastructure against global-scale breakdown.

A remarkable phenomenon in spatiotemporal dynamical systems is chimera state, where the structurally and dynamically identical oscillators in a coupled networked system spontaneously break into two groups, one exhibiting coherent motion and another incoherent. This phenomenon was typically studied in the setting of non-local coupling configurations. We ask what can happen to chimera states under systematic changes to the network structure when links are removed from the network in an orderly fashion but the local coupling topology remains invariant with respect to an index shift. We find the emergence of multicluster chimera states. Remarkably, as a parameter characterizing the amount of link removal is increased, chimera states of distinct numbers of clusters emerge and persist in different parameter regions. We develop a phenomenological theory, based on enhanced or reduced interactions among oscillators in different spatial groups, to explain why chimera states of certain numbers of clusters occur in certain parameter regions. The theoretical prediction agrees well with numerics.

Successful identification of directed dynamical influence in complex systems is relevant to significant problems of current interest. Traditional methods based on Granger causality and transfer entropy have issues such as difficulty with nonlinearity and large data requirement. Recently a framework based on nonlinear dynamical analysis was proposed to overcome these difficulties. We find, surprisingly, that noise can counterintuitively enhance the detectability of directed dynamical influence. In fact, intentionally injecting a proper amount of asymmetric noise into the available time series has the unexpected benefit of dramatically increasing confidence in ascertaining the directed dynamical influence in the underlying system. This result is established based on both real data and model time series from nonlinear ecosystems. We develop a physical understanding of the beneficial role of noise in enhancing detection of directed dynamical influence.

Rationale: Medical masks are commonly used by sick individuals with influenza-like illness (ILI) to prevent spread of infections to others, but clinical efficacy data are absent.

Objective: Determine whether medical mask use by sick individuals with ILI protects well contacts from related respiratory infections.

Setting: 6 major hospitals in 2 districts of Beijing, China.

Design: Cluster randomised controlled trial.

Participants: 245 index cases with ILI.

Intervention: Index cases with ILI were randomly allocated to medical mask (n=123) and control arms (n=122). Since 43 index cases in the control arm also used a mask during the study period, an as-treated post hoc analysis was performed by comparing outcomes among household members of index cases who used a mask (mask group) with household members of index cases who did not use a mask (no-mask group).

Main Outcome Measure: Primary outcomes measured in household members were clinical respiratory illness, ILI and laboratory-confirmed viral respiratory infection.

Results: In an intention-to-treat analysis, rates of clinical respiratory illness (relative risk (RR) 0.61, 95% CI 0.18 to 2.13), ILI (RR 0.32, 95% CI 0.03 to 3.13) and laboratory-confirmed viral infections (RR 0.97, 95% CI 0.06 to 15.54) were consistently lower in the mask arm compared with control, although not statistically significant. A post hoc comparison between the mask versus no-mask groups showed a protective effect against clinical respiratory illness, but not against ILI and laboratory-confirmed viral respiratory infections.

Conclusions: The study indicates a potential benefit of medical masks for source control, but is limited by small sample size and low secondary attack rates. Larger trials are needed to confirm efficacy of medical masks as source control.

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Most previous works on complete synchronization of chaotic oscillators focused on the one-channel interaction scheme where the oscillators are coupled through only one variable or a symmetric set of variables. Using the standard framework of master-stability function (MSF), we investigate the emergence of complex synchronization behaviors under all possible configurations of two-channel coupling, which include, for example, all possible cross coupling schemes among the dynamical variables. Utilizing the classic Rössler and Lorenz oscillators, we find a rich variety of synchronization phenomena not present in any previously extensively studied, single-channel coupling configurations. For example, in many cases two coupling channels can enhance or even generate synchronization where there is only weak or no synchronization under only one coupling channel, which has been verified in a coupled neuron system. There are also cases where the oscillators are originally synchronized under one coupling channel, but an additional synchronizable coupling channel can, however, destroy synchronization. Direct numerical simulations of actual synchronization dynamics verify the MSF-based predictions. Our extensive computation and heuristic analysis provide an atlas for synchronization of chaotic oscillators coupled through two channels, which can be used as a systematic reference to facilitate further research in this area.

The literature including social media shows that Mexican/Latino immigrants have attracted contempt and have been traditionally objected to as a minority in the U.S. The intent here is to search for historical and other factors that might explain the public antipathy and to identify reasons that could, either in isolation or in combination with others, explain anti-immigrant sentiments among people, many of whom are descendants of immigrants. The perusal of the challenges of Mexican immigrants to the U.S. through the decades will highlight some similarities related to discrimination against waves “peoples of color”, not only in the U.S. but in other parts of the world. The daily treatment within the society of immigrants of color as well as the frequent lower immigration quotas imposed on certain groups, including Mediterranean people, makes the topic quite relevant to today’s concerns.

This article presents an exploration of the relationship between community building and lifelong learning. Using a reflective style, the authors propose that the fusion of community building principles with lifelong learning practice can positively transform educational practice. Seven positive pursuits are highlighted regarding their potential to assist the implementation of community building into lifelong learning programs: (1) asset-based thinking; (2) critical reflection; (3) systems thinking; (4) cognitive vibrancy, (5) inclusiveness; (6) creative expression; and, (7) purpose in life. These pursuits draw upon the power of the community development field to bring about more positive transformative moments for individuals and communities participating in lifelong learning programs. The metaphor of bread making is used to illustrate how such transformative moments occur and why they are meaningful to individuals pursuing lifelong learning.