In-Stream Leaf Decomposition as an Indicator of Marcellus Shale Impairment Across a Land Use Gradient

Rapid development of hydrofracking, particularly in the Marcellus Shale region, has greatly outpaced ecological research assessing potential impacts on aquatic ecosystems. Increased sedimentation and contamination of streams from unconventional natural gas (UNG) activity could affect stream biota, resulting in altered rates of in-stream leaf decomposition. We deployed leaf packs in seven sites representing a range of UNG activity among different land uses including forest, agriculture, and development. In addition, physical and chemical variables were measured. Summer breakdown rates for all sites, mesh sizes, and leaf species were higher in the presence of UNG activity. Fall breakdown rates demonstrated no consistent trend among land uses or UNG activity. Summer deployment had more storm events than fall, promoting more runoff into streams as well as more sediment release. This suggests that higher physical breakdown rates in UNG sites could have been caused by more disturbed land, modifying stream hydrology. However, fall measurements, under more consistent flow regimes, indicate sites with flashier hydrology are prone to faster breakdown rates due to mechanical fragmentation rather than biological decomposition. Leaf breakdown rates were not a consistent indicator of UNG impairment among our sites due to factors affecting breakdown rates caused by land uses other than UNG and physical breakdown attributed to hydrologic disturbances

Thermoviscoplastic analysis of fibrous periodic composites using triangular subvolumes

The nonlinear viscoplastic behavior of fibrous periodic composites is analyzed by discretizing the unit cell into triangular subvolumes. A set of these subvolumes can be configured by the analyst to construct a representation for the unit cell of a periodic composite. In each step of the loading history, the total strain increment at any point is governed by an integral equation which applies to the entire composite. A Fourier series approximation allows the incremental stresses and strains to be determined within a unit cell of the periodic lattice. The nonlinearity arising from the viscoplastic behavior of the constituent materials comprising the composite is treated as fictitious body force in the governing integral equation. Specific numerical examples showing the stress distributions in the unit cell of a fibrous tungsten/copper metal matrix composite under viscoplastic loading conditions are given. The stress distribution resulting in the unit cell when the composite material is subjected to an overall transverse stress loading history perpendicular to the fibers is found to be highly heterogeneous, and typical homogenization techniques based on treating the stress and strain distributions within the constituent phases as homogeneous result in large errors under inelastic loading conditions

Institute Brief: Supporting Individuals with Autism Spectrum Disorders: Quality Employment Practices

It has been known for decades that individuals with autism spectrum disorders (ASD), including those with significant impairment or who have behaviors that others find challenging, can work when they are given appropriate supports. It is also clear that individuals with ASD can benefit from employment. Benefits include improved emotional state, greater financial gain, decreased anxiety, greater self-esteem, and greater independence. Nonetheless, employment outcomes for individuals with ASD have traditionally been poor. Even those who do find work are often underemployed or do not hold onto jobs for a long period of time

Consumer Research Needs from the Food and Drug Administration on Front-of-Package Nutritional Labeling

Americans have increasingly busy lifestyles and desire quick and nutritious food choices. To provide consumers with at-a-glance nutrition information, many food manufacturers have introduced front-of-package (FOP) nutritional labeling systems. The purpose of this review is to reach out to the marketing and public policy discipline by identifying research needs on FOP systems not only to aid decision making for federal agencies, but also to help advance research on this important topic. We describe the many FOP systems, the FDA\u27s regulatory background and approach to FOP systems, recent experimental research and gaps in knowledge, and research needs on FOP nutrition labeling

Bayesian Nonparametric Inference of Switching Linear Dynamical Systems

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index, and a maneuvering target tracking application.Comment: 50 pages, 7 figure

A sticky HDP-HMM with application to speaker diarization

We consider the problem of speaker diarization, the problem of segmenting an audio recording of a meeting into temporal segments corresponding to individual speakers. The problem is rendered particularly difficult by the fact that we are not allowed to assume knowledge of the number of people participating in the meeting. To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006) 1566--1581]. Although the basic HDP-HMM tends to over-segment the audio data---creating redundant states and rapidly switching among them---we describe an augmented HDP-HMM that provides effective control over the switching rate. We also show that this augmentation makes it possible to treat emission distributions nonparametrically. To scale the resulting architecture to realistic diarization problems, we develop a sampling algorithm that employs a truncated approximation of the Dirichlet process to jointly resample the full state sequence, greatly improving mixing rates. Working with a benchmark NIST data set, we show that our Bayesian nonparametric architecture yields state-of-the-art speaker diarization results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS395 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

‘Collective intelligence’ is not necessarily present in virtual groups

When we communicate online, we miss an important element of group intelligence: social sensitivity, write Jordan B. Barlow and Alan R. Denni