Modeling the adoption of innovations in the presence of geographic and media influences

While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. In this article, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homopholy both amongst individuals with similar propensities to adopt a technology and geographic location are critical to reproduce features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves

What Determines the Formal Versus Relational Nature of Local Government Contracting?

Meeyoung Lamothe is currently an assistant professor at the University of Oklahoma. Her research interests include local alternative service delivery arrangements, social service contracting, and nonprofit management. Her recent publications may be found in the Journal of Public Administration Research and Theory, International Journal of Public Administration, and American Review of Public Administration.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

IPCC, 2023: Climate Change 2023: Synthesis Report, Summary for Policymakers. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland.

This Synthesis Report (SYR) of the IPCC Sixth Assessment Report (AR6) summarises the state of knowledge of climate change, its widespread impacts and risks, and climate change mitigation and adaptation. It integrates the main findings of the Sixth Assessment Report (AR6) based on contributions from the three Working Groups1 , and the three Special Reports. The summary for Policymakers (SPM) is structured in three parts: SPM.A Current Status and Trends, SPM.B Future Climate Change, Risks, and Long-Term Responses, and SPM.C Responses in the Near Term.This report recognizes the interdependence of climate, ecosystems and biodiversity, and human societies; the value of diverse forms of knowledge; and the close linkages between climate change adaptation, mitigation, ecosystem health, human well-being and sustainable development, and reflects the increasing diversity of actors involved in climate action. Based on scientific understanding, key findings can be formulated as statements of fact or associated with an assessed level of confidence using the IPCC calibrated language

Simulated adoption treating the media as endogenous and increasing with the number of adopters.

<p>(a.) Shows simulated new users per week (normalized to the maximum over the period) as well as normalized media volume each week. (b.) A comparison of all model scenarios is shown. Traditional models, models which do not include media influence are capable of predicting adoption in early periods, but dramatically underestimate total adoption. Including endogenous media effects allows us to make adoption predictions that more closely resemble real data.</p

Plots of weekly adoption for select cities.

<p>(a.) Time series display the number of new U.S. Twitter users for three separate locations (Ann Arbor, MI, Denver, CO, and Arlington, VA) from mid-March 2006 through late-August 2009, normalized by the largest weekly increase in Denver users. (b.) Shows a plot of the cumulative fraction of each city's user base normalized by the total number of users in Denver, CO.</p

Verification of the basic SI model.

<p>Four different transmission rates are displayed, each run 500 times and averaged. The bands surrounding the average value are bounds containing 75%, and 95% of simulation runs.</p

Plots of weekly national adoption.

<p>(a.) The number of new U.S. Twitter users is plotted for each week, normalized by the maximum weekly increase during the entire period of data collection. (b.) The cumulative total number of U.S. Twitter users is plotted for for the same time period. Google search and news volumes are normalized such that the maximum value is 1.</p

Temporal snapshots of critical mass achievement at locations across the US.

<p>For snapshot, the smaller, gray markers indicate locations that have already reached critical mass. The larger, black markers denote locations that achieved critical mass during that week. We note that locations achieving critical mass at very early times are clustered around Twitter's birthplace, San Francisco, CA, suggesting local word-of-mouth diffusion. There are, however, a few locations on the other side of the country, namely the suburbs of Boston, MA that are equally early in adoption, contrasting local diffusion with the flattening effects of the Internet.</p

Simulated critical mass achievement times are compared to times measured from Twitter data.

<p>We find spatially embedded friendships are necessary to reproduce the inter-city spread of Twitter.</p

Gene expression profiles of diabetic kidney disease and neuropathy in eNOS knockout mice: Predictors of pathology and RAS blockade effects

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are two common diabetic complications. However, their pathogenesis remains elusive and current therapies are only modestly effective. We evaluated genome‐wide expression to identify pathways involved in DKD and DPN progression in db/db eNOS−/− mice receiving renin‐angiotensin‐aldosterone system (RAS)‐blocking drugs to mimic the current standard of care for DKD patients. Diabetes and eNOS deletion worsened DKD, which improved with RAS treatment. Diabetes also induced DPN, which was not affected by eNOS deletion or RAS blockade. Given the multiple factors affecting DKD and the graded differences in disease severity across mouse groups, an automatic data analysis method, SOM, or self‐organizing map was used to elucidate glomerular transcriptional changes associated with DKD, whereas pairwise bioinformatic analysis was used for DPN. These analyses revealed that enhanced gene expression in several pro‐inflammatory networks and reduced expression of development genes correlated with worsening DKD. Although RAS treatment ameliorated the nephropathy phenotype, it did not alter the more abnormal gene expression changes in kidney. Moreover, RAS exacerbated expression of genes related to inflammation and oxidant generation in peripheral nerves. The graded increase in inflammatory gene expression and decrease in development gene expression with DKD progression underline the potentially important role of these pathways in DKD pathogenesis. Since RAS blockers worsened this gene expression pattern in both DKD and DPN, it may partly explain the inadequate therapeutic efficacy of such blockers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167018/1/fsb221467_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167018/2/fsb221467.pd