Unraveling the Structure and Bonding Evolution of the Newly Discovered Iron Oxide FeO2

Recently reported synthesis of FeO2 at high pressure has stimulated great interest in exploring this new iron oxide and elucidating its properties. Here, we present a systematic computational study of crystal structure, chemical bonding, and sound velocity of FeO2 in a wide range of pressure. Our results establish thermodynamic stability of the experimentally observed pyrite phase (P-phase) of FeO2 at pressures above 74 GPa and unveil two metastable FeO2 phases in Pbcn and P4(2)/mnm symmetry at lower pressures. Simulated x-ray diffraction (XRD) spectra of Pbcn and P4(2)/mnm FeO2 match well with measured XRD data of the decompression products of P-phase FeO2, providing compelling evidence for the presence of these metastable phases. Energetic calculations reveal unusually soft O-O bonds in P-phase FeO2 stemming from a low-frequency libration mode of FeO6 octahedra, rendering the O-O bond length highly sensitive to computational and physical environments. Calculated sound-velocity profiles of P-phase FeO2 are markedly different from those of the Pbcn and P4(2)/mnm phases, underscoring their distinct seismic signatures. Our findings offer insights for understanding the rich structural, bonding, and elastic behaviors of this newly discovered iron oxide

Type of Multimorbidity and Propensity to Seek Care among Elderly Medicare Beneficiaries

Greater propensity to seek care is critical for improving health of elderly individuals with multimorbidity. We used the Medicare Current Beneficiary Survey (2012) to assess propensity to seek care among community-dwelling elderly Medicare beneficiaries (\u3e 65 yrs.; N=11,270) having (1) no physical or mental illness; (2) single physical or mental condition; (3) multimorbidity with physical conditions only; and (4) multimorbidity with both physical and mental conditions. As compared to multimorbidity with physical conditions, elderly with no multimorbidity were less likely (Adjusted Odds Ratio [95% CI]: 0.50 [0.36, 0.68]) and elderly with both physical and mental conditions were more likely (1.57 [1.28, 1.93]) to have a health problem for which they should have seen a doctor but did not. Further, elderly having a usual source of care were less likely (0.53 [0.37, 0.75]) to have a health problem for which they should have seen a doctor but did not. Multimorbidity is negatively associated with propensity to seek care. The presence of both chronic mental and physical conditions worsened propensity to seek care among elderly individuals. Future efforts to increase the awareness of receiving timely care and improve the access to care can enhance propensity to seek care among elderly individuals with multimorbidity

Electricity Competition and the Public Good: Rethinking Markets and Monopolies

The United States electricity sector is engaged in a long-term experiment regarding the proper role of market competition. Many states that transitioned to competitive electricity markets in the early 2000s are again reconsidering the relationship between market competition and public policy goals. Low natural gas prices, falling costs of renewable energy and energy storage, and improvements in efficiency are causing early retirements of coal and nuclear power plants and thus affecting environmental policy goals and economic interests. States that continue to rely on monopoly utilities for electricity are also reconsidering the role of competition, but from a different angle. Rather than focusing on mitigating the downsides of competition, some traditionally regulated states are creating new opportunities for third parties to compete with monopoly utilities. The implications for electricity sectors in restructured and traditionally regulated states extend far beyond the particular facilities that stand to gain from new subsidies or the monopoly utilities subject to new forms of competition. Post hoc changes to market rules risk wasting resources that will be necessary to aggressively reduce greenhouse gas emissions, ensure long-term affordability, and mitigate the employment impacts of a transitioning sector. This Article explores the factors causing policymakers to reconsider the role of competition in the pursuit of energy goals. It identifies lessons for realizing the benefits of electricity sector competition while managing the downsides that occur during periods of unanticipated change. In restructured markets, the lessons center on strategies to address job losses and achieve state environmental goals. In traditionally regulated states, the lessons focus on opportunities to harness competition to deliver additional societal benefits without undermining the traditional rate-setting model for monopoly utilities

UNLV STARS unabridged report

University of Nevada, Las Vegas, by earning a 2011 Silver Rating in the Sustainability Tracking, Assessment & Ratings System (STARS), is ahead of the curve among public universities – and improving. STARS is a voluntary, self–reporting framework developed by the Association for the Advancement of Sustainability in Higher Education (AASHE) to help measure sustainability performance over time and among colleges and universities nationwide. The UNLV Sustainability Council oversaw STARS, which by measuring sustainability can help UNLV to reduce energy consumption and waste, improve education, attract research, and generate jobs. A rating of Silver puts UNLV in great company – with Yale, University of North Carolina, Oregon, Texas, Florida, and Michigan State – and improving. A Few Highlights UNLV has secured more than $99 million in funding for renewable energy research over the past 10 years. UNLV scored high marks for its new Renewable Energy Minor and Graduate Cirtificate program. Greenspun Hall earned LEED Gold and the Science and Engineering Building scored LEED Silver designations. Grounds and Landscape earned a perfect score. Rebel Recycling diverted 60% of all waste. STARS will serve as a road map for a Comprehensive UNLV Sustainability Action Plan

Productivity of Gas Condensate Fields Below The Dew Point: A North Sea Case Study

Imperial Users onl

Effects of Judicial Primary Election Systems on Challenger Emergence and Candidate Success

While rarely studied, primary elections have a tremendous affect on the general election. This effect can be magnified by institutional differences in the way primary and general elections operate in the states. In the case of judicial elections, the effects of the primary are further confounded by the differences in judicial selection systems across the states. My goal is to understand the role of the primary election as a stepping stone on the way to office. This dissertation endeavors to answer three questions: 1. What are the relevant differences between judicial primary election systems? 2. What influences challengers to emerge in judicial primary elections? 3. How do women move through judicial primary elections to the general election? Using a new, original dataset of judicial primary elections from 1990-2016, I isolated the relevant differences and identified five different types of primary elections held in judicial contests. Challengers are more likely to appear in races where the incumbent had faced challengers in prior primary elections. In the aggregate, women do not face systematic disadvantages in primary elections and win primary contests at high rates. These findings add to the scholarly understanding of judicial elections and prompt further studies on the role of the primary in state judicial and legislative elections

Potential for using climate forecasts in spatio-temporal prediction of dengue fever incidence in Malaysia submitted

Dengue fever is a viral infection transmitted by the bite of female Aedes aegypti mosquitoes. It is estimated that nearly 40% of the world’s population is now at risk from Dengue in over 100 endemic countries including Malaysia. Several studies in various countries in recent years have identified statistically significant links between Dengue incidence and climatic factors. There has been relatively little work on this issue in Malaysia, particularly on a national scale. This study attempts to fill that gap. The primary research question is ‘to what extent can climate variables be used to assist predictions of dengue fever incidence in Malaysia?’. The study proposes a potential framework of modelling spatio-temporal variation in dengue risk on a national scale in Malaysia using both climate and non-climate information. Early chapters set the scene by discussing Malaysia and Climate in Malaysia and reviewing previous work on dengue fever and dengue fever in Malaysia. Subsequent chapters focus on the analysis and modelling of annual dengue incidence rate (DIR) for the twelve states of Peninsular Malaysia for the period 1991 to 2009 and monthly DIR for the same states in the period 2001 to 2009. Exploratory analyses are presented which suggest possible relationships between annual and monthly DIR and climate and other factors. The variables that were considered included annual trend, in year seasonal effects, population, population density and lagged dengue incidence rate as well as climate factors such as average rainfall and temperature, number of rainy days, ENSO and lagged values of these climate variables. Findings include evidence of an increasing annual trend in DIR in all states of Malaysia and a strong in-year seasonal cycle in DIR with possible differences in this cycle in different geographical regions of Malaysia. High population density is found to be positively related to monthly DIR as is the DIR in the immediately preceding months. Relationships between monthly DIR and climate variables are generally quite weak, nevertheless some relationships may be able to be usefully incorporated into predictive models. These include average temperature and rainfall, number of rainy days and ENSO. However lagged values of these variables need to be considered for up to 6 months in the case of ENSO and from 1-3 months in the case of other variables. These exploratory findings are then more formally investigated using a framework where dengue counts are modelled using a negative binomial generalised linear model (GLM) with a population offset. This is subsequently extended to a negative binomial generalised additive model (GAM) which is able to deal more flexibly with non-linear relationships between the response and certain of the explanatory variables. The model successfully accounts for the large amount of overdispersion found in the observed dengue counts. Results indicated that there are statisti�cally significant relationships with both climate and non-climate covariates using this modelling framework. More specifically, smooth functions of year and month differentiated by geographical areas of the country are significant in the model to allow for seasonality and annual trend. Other significant covariates included were mean rainfall at lag zero month and lag 3 months, mean temperature at lag zero month and lag 1 month, number of rainy days at lag zero month and lag 3 months, sea surface temperature at lag 6 months, interaction between mean temperature at lag 1 month and sea surface temperature at lag 6 months, dengue incidence rate at lag 3 months and population density. Three final competing models were selected as potential candidates upon which an early warning system for dengue in Malaysia might be able to be developed. The model fits for the whole data set were compared using simulation experiments to allow for both parameter and negative binomial model uncertainty and a single model preferred from the three models was identified. The ‘out of sample’ predictive performance of this model was then compared and contrasted for different lead times by fitting the model to the first 7 years of the 9 years monthly data set covering 2001-2009 and then analysing predictions for the subsequent 2 years for lead time of 3, 6 12 and 24 months. Again simulation experiments were conducted to allow for both parameter and model uncertainty. Results were mixed. There does seem to be predictive potential for lead times of up to six months from the model in areas outside of the highly urbanised South Western states of Kuala Lumpur and Selangor and such a model may therefore possibly be useful as a basis for developing early warning systems for those areas. However, none of the models developed work well for Kuala Lumpur and Selangor where there are clearly more complex localised influences involved which need further study. This study is one of the first to look at potential climatic influences on dengue incidence on a nationwide scale in Malaysia. It is also one of the few studies worldwide to explore the use of generalised additive models in the spatio-temporal modelling of dengue incidence. Although, the results of the study show a mixed picture, hopefully the framework developed will be able to be used as a starting point to investigate further if climate information can valuably be incorporated in an early warning system for dengue in Malaysi