On the stability of 2 \sqrt{2} x 2 \sqrt{2} oxygen ordered superstructures in YBa2Cu3O6+x

We have compared the ground-state energy of several observed or proposed " 2 \sqrt{2} x 2 \sqrt{2} oxygen (O) ordered superstructures " (from now on HS), with those of "chain superstructures" (CS) (in which the O atoms of the basal plane are ordered in chains), for different compositions x in YBa2Cu3O6+x. The model Hamiltonian contains i) the Madelung energy, ii) a term linear in the difference between Cu and O hole occupancies which controls charge transfer, and iii) covalency effects based on known results for $t-J$ models in one and two dimensions. The optimum distribution of charge is determined minimizing the total energy, and depends on two parameters which are determined from known results for x=1 and x=0.5. We obtain that on the O lean side, only CS are stable, while for x=7/8, a HS with regularly spaced O vacancies added to the x=1 structure is more stable than the corresponding CS for the same x. We find that the detailed positions of the atoms in the structure, and long-range Coulomb interactions, are crucial for the electronic structure, the mechanism of charge transfer, the stability of the different phases, and the possibility of phase separation.Comment: 24 text pages, Latex, one fig. included as ps file, to be publisheb in Phys. Rev.

Uncertainties in the Application of Rock Mass Classification and Geomechanical Models for Engineering Design in Carbonate Rocks

The main limits and uncertainties in the implementation of existing geomechanical classifications to carbonate rocks are dealt with in this paper. Starting from the peculiarities of carbonates, mostly expressed by the presence of a complex network of karst conduits and caves produced by dissolution processes, the need toward a specific classification for this category of rocks is presented, together with considerations coming out from the critical analysis of the literature

Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study

Background Socioeconomic status is associated with differences in risk factors for cardiovascular disease incidence and outcomes, including mortality. However, it is unclear whether the associations between cardiovascular disease and common measures of socioeconomic status—wealth and education—differ among high-income, middle-income, and low-income countries, and, if so, why these differences exist. We explored the association between education and household wealth and cardiovascular disease and mortality to assess which marker is the stronger predictor of outcomes, and examined whether any differences in cardiovascular disease by socioeconomic status parallel differences in risk factor levels or differences in management. Methods In this large-scale prospective cohort study, we recruited adults aged between 35 years and 70 years from 367 urban and 302 rural communities in 20 countries. We collected data on families and households in two questionnaires, and data on cardiovascular risk factors in a third questionnaire, which was supplemented with physical examination. We assessed socioeconomic status using education and a household wealth index. Education was categorised as no or primary school education only, secondary school education, or higher education, defined as completion of trade school, college, or university. Household wealth, calculated at the household level and with household data, was defined by an index on the basis of ownership of assets and housing characteristics. Primary outcomes were major cardiovascular disease (a composite of cardiovascular deaths, strokes, myocardial infarction, and heart failure), cardiovascular mortality, and all-cause mortality. Information on specific events was obtained from participants or their family. Findings Recruitment to the study began on Jan 12, 2001, with most participants enrolled between Jan 6, 2005, and Dec 4, 2014. 160 299 (87·9%) of 182 375 participants with baseline data had available follow-up event data and were eligible for inclusion. After exclusion of 6130 (3·8%) participants without complete baseline or follow-up data, 154 169 individuals remained for analysis, from five low-income, 11 middle-income, and four high-income countries. Participants were followed-up for a mean of 7·5 years. Major cardiovascular events were more common among those with low levels of education in all types of country studied, but much more so in low-income countries. After adjustment for wealth and other factors, the HR (low level of education vs high level of education) was 1·23 (95% CI 0·96–1·58) for high-income countries, 1·59 (1·42–1·78) in middle-income countries, and 2·23 (1·79–2·77) in low-income countries (pinteraction<0·0001). We observed similar results for all-cause mortality, with HRs of 1·50 (1·14–1·98) for high-income countries, 1·80 (1·58–2·06) in middle-income countries, and 2·76 (2·29–3·31) in low-income countries (pinteraction<0·0001). By contrast, we found no or weak associations between wealth and these two outcomes. Differences in outcomes between educational groups were not explained by differences in risk factors, which decreased as the level of education increased in high-income countries, but increased as the level of education increased in low-income countries (pinteraction<0·0001). Medical care (eg, management of hypertension, diabetes, and secondary prevention) seemed to play an important part in adverse cardiovascular disease outcomes because such care is likely to be poorer in people with the lowest levels of education compared to those with higher levels of education in low-income countries; however, we observed less marked differences in care based on level of education in middle-income countries and no or minor differences in high-income countries. Interpretation Although people with a lower level of education in low-income and middle-income countries have higher incidence of and mortality from cardiovascular disease, they have better overall risk factor profiles. However, these individuals have markedly poorer health care. Policies to reduce health inequities globally must include strategies to overcome barriers to care, especially for those with lower levels of education. Funding Full funding sources are listed at the end of the paper (see Acknowledgments)