Persuading young consumers to make healthy nutritional decisions.

There is widespread concern that consumers are making inappropriate decisions about what they eat, leading to a growing incidence of obesity and chronic illness which will strain public health budgets and damage economic competitiveness. Inappropriate nutritional decisions and obesity are of particular public policy importance where young consumers are concerned. The paper investigates how consumers, particularly young consumers, can be persuaded to make better nutritional decisions voluntarily, and how government and commercial persuasive communications can be deployed to facilitate such decisions. The key conclusions are that the mass media are not a reliable vehicle for bringing about the desired behavioural changes, but that new media, such as the Internet and ‘text messaging’ should be used to deliver tailored messages to individuals, particularly younger consumers

Looking for evidence of noncompetitive behavior in Minnesota's banking industry

Banks and banking - Minnesota

Dilaton-Axion hair for slowly rotating Kerr black holes

Campbell et al. demonstrated the existence of axion ``hair'' for Kerr black holes due to the non-trivial Lorentz Chern-Simons term and calculated it explicitly for the case of slow rotation. Here we consider the dilaton coupling to the axion field strength, consistent with low energy string theory and calculate the dilaton ``hair'' arising from this specific axion source.Comment: 13 pages + 1 fi

USDA Farm Programs: North Dakota Farmer Participation and Opinions

Agricultural and Food Policy,

Network Inference via the Time-Varying Graphical Lasso

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

Sustainable Agriculture and the Structure of North Dakota Agriculture

Environmental Economics and Policy, Industrial Organization, Production Economics,

Selected Characteristics of North Dakota Farm Families Engaged in Sustainable Agricultural Practices

Environmental Economics and Policy, Farm Management, Resource /Energy Economics and Policy,