Increasing Awareness of the HPV Vaccine

Increasing the public awareness and knowledge of HPV complications and prevention through vaccination is an effective intervention to increase vaccination rates and reduce overall public health cost and burden.https://scholarworks.uvm.edu/fmclerk/1141/thumbnail.jp

How resource-efficient is the global steel industry?

Resource efficiency is an instrumental mitigation option in the steel industry but, existing studies have failed to provide a global analysis of the sector's energy and material use. Despite the interactions between energy and materials in steelmaking, recent studies investigate each of these resources in isolation, providing only partial insight into resource efficiency. This study analyses the latest, most comprehensive resource data on the global steel industry and quantifies the savings associated with reducing this through energy- and material- saving measures. Three production routes are investigated for 2010, namely the blast furnace/basic oxygen furnace (ore-based); direct reduction/electric arc furnace; scrap-based electric arc furnace routes (secondary). The sector's resource efficiency - accounting for energy and materials - is expressed in exergy and measured at two levels, that of production routes and plants. The results show that the sector is 32.9% resource-efficient and that secondary steelmaking is twice as efficient (65.7%) as ore-based production (29.1%). Energy-saving options, such as the recovery of off-gases, can save about 4 EJ/year (exergy). Material- saving options, such as yield improvements, can deliver just under 1 EJ/year extra. A global shift from average ore-based production to best available operation can save up to 6.4 EJ/year; a 26% reduction in global exergy input to steelmaking. Shifting to secondary steelmaking can save 8 EJ/year, limited only by the need to still produce half of steel from ore in 2050. Resource efficiency, measured in exergy, provides stakeholders with an instrument that treats energy and material efficiency measures on an equal footing

El Museo de las Conceptas: Assessing and Advancing Community Engagement

The goal of this project was to develop a plan that would increase the local communityÂ’s awareness of and involvement with el Museo de las Conceptas, a religious museum in Cuenca, Ecuador devoted to the history of the attached monastery. To this end, we conducted surveys with museum visitors and the general public and interviewed local museum professionals in order to discern the best course of action. From these interviews, we decided that creating a cohesive narrative to improve the visitor experience paired with a marketing campaign would be the best solution to the museumÂ’s challenges

Masses and Mixings in a Grand Unified Toy Model

The generation of the fermion mass hierarchy in the standard model of particle physics is a long-standing puzzle. The recent discoveries from neutrino physics suggests that the mixing in the lepton sector is large compared to the quark mixings. To understand this asymmetry between the quark and lepton mixings is an important aim for particle physics. In this regard, two promising approaches from the theoretical side are grand unified theories and family symmetries. In this note we try to understand certain general features of grand unified theories with Abelian family symmetries by taking the simplest SU(5) grand unified theory as a prototype. We construct an SU(5) toy model with $U(1)_F \otimes Z'_2\otimes Z''_2 \otimes Z'''_2$ family symmetry that, in a natural way, duplicates the observed mass hierarchy and mixing matrices to lowest approximation. The system for generating the mass hierarchy is through a Froggatt-Nielsen type mechanism. One idea that we use in the model is that the quark and charged lepton sectors are hierarchical with small mixing angles while the light neutrino sector is democratic with larger mixing angles. We also discuss some of the difficulties in incorporating finer details into the model without making further assumptions or adding a large scalar sector.Comment: 21 pages, 2 figures, RevTeX, v2: references updated and typos corrected, v3: updated top quark mass, comments on MiniBooNE result, and typos correcte

Learning on a Budget Using Distributional RL

Agents acting in real-world scenarios often have constraints such as finite budgets or daily job performance targets. While repeated (episodic) tasks can be solved with existing RL algorithms, methods need to be extended if the repetition depends on performance. Recent work has introduced a distributional perspective on reinforcement learning, providing a model of episodic returns. Inspired by these results we contribute the new budget- and risk-aware distributional reinforcement learning (BRAD-RL) algorithm that bootstraps from the C51 distributional output and then uses value iteration to estimate the value of starting an episode with a certain amount of budget. With this strategy we can make budget-wise action selection within each episode and maximize the return across episodes. Experiments in a grid-world domain highlight the benefits of our algorithm, maximizing discounted future returns when low cumulative performance may terminate repetition

A Bayesian methodology for estimating uncertainty of decisions in safety-critical systems

Published as chapter in Frontiers in Artificial Intelligence and Applications. Volume 149, IOS Press Book, 2006. Integrated Intelligent Systems for Engineering Design. Edited by Xuan F. Zha, R.J. Howlett. ISBN 978-1-58603-675-1, pp. 82-96. This version deposited in arxiv.orghttp://arxiv.org/abs/1012.0322Uncertainty of decisions in safety-critical engineering applications can be estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) technique of averaging over decision models. The use of decision tree (DT) models assists experts to interpret causal relations and find factors of the uncertainty. Bayesian averaging also allows experts to estimate the uncertainty accurately when a priori information on the favored structure of DTs is available. Then an expert can select a single DT model, typically the Maximum a Posteriori model, for interpretation purposes. Unfortunately, a priori information on favored structure of DTs is not always available. For this reason, we suggest a new prior on DTs for the Bayesian MCMC technique. We also suggest a new procedure of selecting a single DT and describe an application scenario. In our experiments on the Short-Term Conflict Alert data our technique outperforms the existing Bayesian techniques in predictive accuracy of the selected single DTs.Supported by a grant from the EPSRC under the Critical Systems Program, grant GR/R24357/0

Mixed Valvular Disease Following Transcatheter Aortic Valve Replacement: Quantification and Systematic Differentiation Using Clinical Measurements and Image-Based Patient‐Specific In Silico Modeling

Background: Mixed valvular disease (MVD), mitral regurgitation (MR) from pre‐existing disease in conjunction with paravalvular leak (PVL) following transcatheter aortic valve replacement (TAVR), is one of the most important stimuli for left ventricle (LV) dysfunction, associated with cardiac mortality. Despite the prevalence of MVD, the quantitative understanding of the interplay between pre‐existing MVD, PVL, LV, and post‐TAVR recovery is meager. Methods and Results: We quantified the effects of MVD on valvular‐ventricular hemodynamics using an image‐based patient‐specific computational framework in 72 MVD patients. Doppler pressure was reduced by TAVR (mean, 77%; N=72; P<0.05), but it was not always accompanied by improvements in LV workload. TAVR had no effect on LV workload in 22 patients, and LV workload post‐TAVR significantly rose in 32 other patients. TAVR reduced LV workload in only 18 patients (25%). PVL significantly alters LV flow and increases shear stress on transcatheter aortic valve leaflets. It interacts with mitral inflow and elevates shear stresses on mitral valve and is one of the main contributors in worsening of MR post‐TAVR. MR worsened in 32 patients post‐TAVR and did not improve in 18 other patients. Conclusions: PVL limits the benefit of TAVR by increasing LV load and worsening of MR and heart failure. Post‐TAVR, most MVD patients (75% of N=72; P<0.05) showed no improvements or even worsening of LV workload, whereas the majority of patients with PVL, but without that pre‐existing MR condition (60% of N=48; P<0.05), showed improvements in LV workload. MR and its exacerbation by PVL may hinder the success of TAVR

Experimental Comparison of Classification Uncertainty for Randomised and Bayesian Decision Tree Ensembles

Copyright © 2004 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Intelligent Data Engineering and Automated Learning – IDEAL 20045th International Conference on Intelligent Data Engineering and Automated Learning – IDEAL 2004, Exeter, UK. August 25-27, 2004In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class posterior distribution and a given confidence probability. Counting the classifier outcomes, this technique produces feasible evaluations of the classification uncertainty. Using this technique in our experiments, we found that the Bayesian DT technique is superior to the randomised DT ensemble technique

Estimating Classification Uncertainty of Bayesian Decision Tree Technique on Financial Data

Copyright © 2007 Springer. The final publication is available at link.springer.comBook title: Perception-based Data Mining and Decision Making in Economics and FinanceSummary Bayesian averaging over classification models allows the uncertainty of classification outcomes to be evaluated, which is of crucial importance for making reliable decisions in applications such as financial in which risks have to be estimated. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the diversity of a classifier ensemble and the required performance. The interpretability of classification models can also give useful information for experts responsible for making reliable classifications. For this reason Decision Trees (DTs) seem to be attractive classification models. The required diversity of the DT ensemble can be achieved by using the Bayesian model averaging all possible DTs. In practice, the Bayesian approach can be implemented on the base of a Markov Chain Monte Carlo (MCMC) technique of random sampling from the posterior distribution. For sampling large DTs, the MCMC method is extended by Reversible Jump technique which allows inducing DTs under given priors. For the case when the prior information on the DT size is unavailable, the sweeping technique defining the prior implicitly reveals a better performance. Within this chapter we explore the classification uncertainty of the Bayesian MCMC techniques on some datasets from the StatLog Repository and real financial data. The classification uncertainty is compared within an Uncertainty Envelope technique dealing with the class posterior distribution and a given confidence probability. This technique provides realistic estimates of the classification uncertainty which can be easily interpreted in statistical terms with the aim of risk evaluation