A key challenge to understanding the eruption ofglobalization protest since the late 1990s IS the lack of data on the protesters themselves. Although scholars have focused increasingly on these large protest events and the transnational social movements that play a role organizing them, information about the protesters remains scant. We address this research yap by analyzing survey data collectedfrom a random sample ofprotesters atfve globalization protests in three countries By disaggregating protesters from the local area and protesters who traveled to the protest went, the role that organizations play becomes clear: SMOs mobilize non-local participants and coordinate travel to protest even& These data also suggest answers to the broader questions that have emerged about global civil society In contrast to the expectatlons in recent scholarship. we find very Jew protesters came from outside ofthe countries in which the protests were taking place. Instead, we conclude that SMOs use the Internet to connect domestically grounded activists to transnational struggles and to mobilize them to participate in large-scale protest events. In other words, organizations do, indeed, matter in theglobahzation movement and have sign#cantly expanded the protesting population beyond local citizens. In recent years, citizen protests have taken place around the world in response to meetings of international institutions and multilateral regimes. These protests, which are responses to aspects of globalization and expressions of civic dissatisfactoin with global governance, bring about a relatively new type of citizen mobilization: large demonstrations that take place concurrent with these meetings. Scholars have begun to explore the ways in which this movement is different from its predecessors, focusing on its transnational connec

Background Social networks are increasingly recognized as important points of intervention, yet relatively few intervention studies of respiratory infection transmission have utilized a network design. Here we describe the design, methods, and social network structure of a randomized intervention for isolating respiratory infection cases in a university setting over a 10-week period. Methodology/Principal Findings 590 students in six residence halls enrolled in the eX-FLU study during a chain-referral recruitment process from September 2012-January 2013. Of these, 262 joined as “seed” participants, who nominated their social contacts to join the study, of which 328 “nominees” enrolled. Participants were cluster-randomized by 117 residence halls. Participants were asked to respond to weekly surveys on health behaviors, social interactions, and influenza-like illness (ILI) symptoms. Participants were randomized to either a 3-Day dorm room isolation intervention or a control group (no isolation) upon illness onset. ILI cases reported on their isolation behavior during illness and provided throat and nasal swab specimens at onset, day-three, and day-six of illness. A subsample of individuals (N=103) participated in a sub-study using a novel smartphone application, iEpi, which collected sensor and contextually-dependent survey data on social interactions. Within the social network, participants were significantly positively assortative by intervention group, enrollment type, residence hall, iEpi participation, age, gender, race, and alcohol use (all P<0.002). Conclusions/Significance We identified a feasible study design for testing the impact of isolation from social networks in a university setting. These data provide an unparalleled opportunity to address questions about isolation and infection transmission, as well as insights into social networks and behaviors among college-aged students. Several important lessons were learned over the course of this project, including feasible isolation durations, the need for extensive organizational efforts, as well as the need for specialized programmers and server space for managing survey and smartphone data.

Miniaturized planar solid-oxide fuel cells (SOFCs) and stacks can be fabricated by thin film deposition and micromachining. Serious thermal stresses, originating in fabrication and during operation, cause thermal–mechanical instability of the constituent thin films. In this paper, the effect of thin film geometry on thermal stress and mechanical stability is evaluated to optimize the structure of a thin film. A novel design of thin circular electrolyte films for SOFCs is presented by using corrugated structures, with which small thermal stresses and a broad design range of structure parameters can be obtained. Thermal transfer analysis shows that heat loss by solid conduction is serious in thin films with a small radius. But thermal convection and radiation dominate heat loss in large thin films with a radius of several millimetres. Scale-dependent thermal characteristics show the importance of film size and packaging in optimization of thermal isolation for micro SOFCs. A novel flip-flop stack configuration for micro SOFCs is presented. This configuration allows multiple cells to share one reaction chamber, helps to obtain uniform flow fields, and simplifies the flow field network for micro fuel cell stacks.

The development of microbial networks is central to ecosystem functioning and is the hallmark of complex natural systems. Characterizing network development over time and across environmental gradients is hindered by the millions of potential interactions among community members, limiting interpretations of network evolution. We developed a feature selection approach using data winnowing that identifies the most ecologically influential microorganisms within a network undergoing change. Using a combination of graph theory, leave-one-out analysis, and statistical inference, complex microbial communities are winnowed to identify the core organisms responding to external gradients or functionality, and then network development is evaluated against these externalities. In a plant invasion case study, the winnowed microbial network became more influential as the plant invasion progressed as a result of direct plant-microbe links rather than the expected indirect plant-soil-microbe links. This represents the first use of structural equation modeling to predict microbial network evolution, which requires identification of keystone taxa and quantification of the ecological processes underpinning community structure and function patterns.

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods.