Modeling Defects, Shape Evolution, and Programmed Auto-origami in Liquid Crystal Elastomers

Liquid crystal elastomers represent a novel class of programmable shape-transforming materials whose shape change trajectory is encoded in the material's nematic director field. Using three-dimensional nonlinear finite element elastodynamics simulation, we model a variety of different actuation geometries and device designs: thin films containing topological defects, patterns that induce formation of folds and twists, and a bas-relief structure. The inclusion of finite bending energy in the simulation model reveals features of actuation trajectory that may be absent when bending energy is neglected. We examine geometries with a director pattern uniform through the film thickness encoding multiple regions of positive Gaussian curvature. Simulations indicate that heating such a system uniformly produces a disordered state with curved regions emerging randomly in both directions due to the film's up-down symmetry. By contrast, applying a thermal gradient by heating the material first on one side breaks up-down symmetry and results in a deterministic trajectory producing a more ordered final shape. We demonstrate that a folding zone design containing cut-out areas accommodates transverse displacements without warping or buckling; and demonstrate that bas-relief and more complex bent-twisted structures can be assembled by combining simple design motifs.Comment: 11 pages, 7 figure

Public and private provision of infrastructure and economic development.

This paper examines the role of infrastructure in long run economic growth. The paper consists of two sections, the first concentrates on the theoretical role of government spending in models of growth and the second details examples of private participation in infrastructure development. Using a simple endogenous growth model we find that while the hypothesized benefits of infrastructure expenditures may be large they require care in matching appropriate financing. As the development and maintenance of infrastructure will continue to be pivotal to the long term success of growing economies, we emphasize the lessons on financing and the caveats of private participation to those who are exploring innovative mechanisms for infrastructure design.

Ground Movements due to Shallow Tunnels in Soft Ground. I: Analytical Solutions

This paper presents simplified closed-form analytical solutions that can be used to interpret and predict ground movements caused by shallow tunneling in soft ground conditions. These solutions offer a more comprehensive framework for understanding the distribution of ground movements than widely used empirical functions. Analytical solutions for the displacement field within the ground mass are obtained for two basic modes of deformation corresponding to uniform convergence and ovalization at the wall of a circular tunnel cavity, based on the assumption of linear, elastic soil behavior. Deformation fields based on the superposition of fundamental, singularity solutions are shown to differ only slightly from analyses that consider the physical dimensions of the tunnel cavity, except in the case of very shallow tunnels. This work demonstrates a simplified method to account for soil plasticity in the analyses and illustrate closed-form solutions for a three-dimensional (3D) tunnel heading. A companion paper describes applications of these analyses to interpret field measurements of ground response to tunneling.University of Puerto Rico (Mayagüez Campus)Colorado Mining AssociationJacobs AssociatesKiewit-Kenny-Zachry (Contractor

Unveiling the environment surrounding LMXB SAX J1808.4-3658

Low-mass X-ray binaries (LMXBs) are a natural workbench to study accretion disk phenomena and optimal background sources to measure elemental abundances in the Interstellar medium (ISM). In high-resolution XMM-Newton spectra, the LMXB SAX J1808.4-3658 showed in the past a neon column density significantly higher than expected given its small distance, presumably due to additional absorption from a neon-rich circumstellar medium (CSM). It is possible to detect intrinsic absorption from the CSM by evidence of Keplerian motions or outflows. For this purpose, we use a recent, deep (100 ks long), high-resolution Chandra/LETGS spectrum of SAX J1808.4-3658 in combination with archival data. We estimated the column densities of the different absorbers through the study of their absorption lines. We used both empirical and physical models involving photo- and collisional-ionization in order to determine the nature of the absorbers. The abundances of the cold interstellar gas match the solar values as expected given the proximity of the X-ray source. For the first time in this source, we detected neon and oxygen blueshifted absorption lines that can be well modeled with outflowing photoionized gas. The wind is neon rich (Ne/O>3) and may originate from processed, ionized gas near the accretion disk or its corona. The kinematics (v=500-1000 km/s) are indeed similar to those seen in other accretion disks. We also discovered a system of emission lines with very high Doppler velocities (v~24000 km/s) originating presumably closer to the compact object. Additional observations and UV coverage are needed to accurately determine the wind abundances and its ionization structure.Comment: 12 pages, 10 figures, accepted for publication on A&

HST Survey of Clusters in Nearby Galaxies. II. Statistical Analysis of Cluster Populations

We present a statistical system that can be used in the study of cluster populations. The basis of our approach is the construction of synthetic cluster color-magnitude-radius diagrams (CMRDs), which we compare with the observed data using a maximum likelihood calculation. This approach permits a relatively easy incorporation of incompleteness (a function of not only magnitude and color, but also radius), photometry errors and biases, and a variety of other complex effects into the calculation, instead of the more common procedure of attempting to correct for those effects. We then apply this procedure to our NGC 3627 data from Paper I. We find that we are able to successfully model the observed CMRD and constrain a number of parameters of the cluster population. We measure a power law mass function slope of alpha = -1.50 +/- 0.07, and a distribution of core radii centered at r_c = 1.53 +/- 0.15 pc. Although the extinction distribution is less constrained, we measured a value for the mean extinction consistent with that determined in Paper I from the Cepheids.Comment: 21 pages, 3 figures accepted for publication by A

Using concept mapping in the knowledge-to-action process to compare stakeholder opinions on barriers to use of cancer screening among South Asians

BACKGROUND: Using the knowledge-to-action (KTA) process, this study examined barriers to use of evidence-based interventions to improve early detection of cancer among South Asians from the perspective of multiple stakeholders. METHODS: In 2011, we used concept mapping with South Asian residents, and representatives from health service and community service organizations in the region of Peel Ontario. As part of concept mapping procedures, brainstorming sessions were conducted with stakeholders (n = 53) to identify barriers to cancer screening among South Asians. Participants (n = 46) sorted barriers into groups, and rated barriers from lowest (1) to highest (6) in terms of importance for use of mammograms, Pap tests and fecal occult blood tests, and how feasible it would be to address them. Multi-dimensional scaling, cluster analysis, and descriptive statistics were used to analyze the data. RESULTS: A total of 45 unique barriers to use of mammograms, Pap tests, and fecal occult blood tests among South Asians were classified into seven clusters using concept mapping procedures: patient’s beliefs, fears, lack of social support; health system; limited knowledge among residents; limited knowledge among physicians; health education programs; ethno-cultural discordance with the health system; and cost. Overall, the top three ranked clusters of barriers were ‘limited knowledge among residents,’ ‘ethno-cultural discordance,’ and ‘health education programs’ across surveys. Only residents ranked ‘cost’ second in importance for fecal occult blood testing, and stakeholders from health service organizations ranked ‘limited knowledge among physicians’ third for the feasibility survey. Stakeholders from health services organizations ranked ‘limited knowledge among physicians’ fourth for all other surveys, but this cluster consistently ranked lowest among residents. CONCLUSION: The limited reach of cancer control programs to racial and ethnic minority groups is a critical implementation issue that requires attention. Opinions of community service and health service organizations on why this deficit in implementation occurs are fundamental to understanding the solutions because these are the settings in which evidence-based interventions are implemented. Using concept mapping within a KTA process can facilitate the engagement of multiple stakeholders in the utilization of study results and in identifying next steps for action

Predictive-State Decoders: Encoding the Future into Recurrent Networks

Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied on explicitly representing and targeting sufficient statistics of this probability distribution. We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations. Predictive-State Decoders are simple to implement and easily incorporated into existing training pipelines via additional loss regularization. We demonstrate the effectiveness of PSDs with experimental results in three different domains: probabilistic filtering, Imitation Learning, and Reinforcement Learning. In each, our method improves statistical performance of state-of-the-art recurrent baselines and does so with fewer iterations and less data.Comment: NIPS 201