Heat capacities of aqueous sodium hydroxide/aluminate mixtures and prediction of the solubility constant of boehmite up to 300 °C

A modified commercial (Setaram C80) calorimeter has been used to measure the isobaric volumetric heat capacities of concentrated alkaline sodium aluminate solutions at ionic strengths from 1 to 6 mol kg-1, with up to 40 mol.% substitution of hydroxide by aluminate, at temperatures from 50 to 300 °C and a pressure of 10 MPa. Apparent molar heat capacities for the mixtures, Cpφ{symbol}, derived from these data were found to depend linearly on the aluminate substitution level, i.e., they followed Young's rule. These quantities were used to estimate the apparent molar heat capacities of pure, hypothetical sodium aluminate solutions, Cpφ{symbol} ('NaAl(OH)4'(aq)). Slopes of the Young's rule plots were invariant with ionic strength at a given temperature but depended linearly on temperature. The heat capacities of ternary aqueous sodium hydroxide/aluminate mixtures could therefore be modelled using only two parameters in addition to those needed for the correlation of Cpφ{symbol} (NaOH(aq)) reported previously from these laboratories. An assessment of the standard thermodynamic quantities for boehmite, gibbsite and the aluminate ion yielded a set of recommended values that, together with the present heat capacity data, accurately predicts the solubility of gibbsite and boehmite at temperatures up to 300 °C

Analyzing veterinary surveillance data: Approaches to model the relationship between disease incidence and cattle trade

Two approaches to the analysis of registry data for bovine diseases with regard to the relationship between disease incidence and cattle trade are proposed. Firstly, a parameter-driven spatio-temporal disease mapping model formulated in a hierarchical Bayesian framework is used. Various cattle movement parameters, e.g. the number and proportion of in-movements from infected regions, can be included as potential covariates. Within this context problems of such an endogenous covariate are discussed. Since a purely parameter-driven approach is often not adequate to depict local epidemics, a so-called observationdriven infectious disease model is proposed as a second possibility. It includes an autoregressive part for counts in the region of interest in the past. Additionally, the sum of previous cases in other regions weighted by cattle movements is added to assess the spread of the disease by trading. Both models are applied to cases of Coxiellosis in Switzerland, 2005 to 2009

A primer on disease mapping and ecological regression using INLA{\texttt{INLA}}

Spatial and spatio-temporal disease mapping models are widely used for the analysis of registry data and usually formulated in a hierarchical Bayesian framework. Explanatory variables can be included by a so-called ecological regression. It is possible to assume both a linear and a nonparametric association between disease incidence and the explanatory variable. Integrated nested Laplace approximations (INLA) can be used as a tool for Bayesian inference. INLA is a promising alternative to Markov chain Monte Carlo (MCMC) methods which provides very accurate results within short computational time. It is shown in this paper, how parameter estimates for well-known spatial and spatio-temporal models can be obtained by running INLA directly in $${\texttt{R}}$$ using the package $${\texttt{INLA}}$$. Selected $${\texttt{R}}$$ code is shown. An emphasis is given to the inclusion of an explanatory variable. Cases of Coxiellosis among Swiss cows from 2005 to 2008 are used for illustration. The number of stillborn calves is included as time-varying covariate. Additionally, various aspects of INLA such as model choice criteria, computer time, accuracy of the results and usability of the $${\texttt{R}}$$ package are discusse

Bayesian computing with INLA: New features

The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We also discuss the current default method in R-INLA to approximate posterior marginals of the hyperparameters using only a modest number of evaluations of the joint posterior distribution of the hyperparameters, without any need for numerical integration

Efficient separation of rare earths recovered by a supported ionic liquid from bauxite residue leachate

Bauxite residue (BR) contains substantial concentrations of rare-earth elements (REEs), but their recovery is a challenge. Acidic BR leachates typically comprise much higher concentrations of base elements (g L−1) than those of the REEs (ppm). Thus, adsorbents that are highly selective for the REEs over the base elements are required for the separation. The novel supported ionic liquid phase (SILP) betainium sulfonyl(trifluoromethanesulfonylimide) poly(styrene-co-divinylbenzene) [Hbet-STFSI-PS-DVB] was evaluated for the uptake of REEs (Sc, Y, Nd, Dy) in the presence of base elements (Ca, Al, Fe) from BR leachates. Breakthrough curves from acidic nitrate and sulfate media were investigated, as both HNO3 and H2SO4 are commonly used for leaching of BR. The SILP exhibited a superior affinity for REEs in both media, except in the case of Sc(III) from the sulfate feed. The recovery rates of the trace amounts of REEs from the real nitrate feed were remarkably high (71.7–100%) via a simple chromatography separation, without requiring complexing agents or a pretreatment for the removal of interfering elements. The REEs were purified from the base elements and separated into three sub-groups (scandium, light REEs and heavy REEs) by an optimized elution profile with H3PO4 and HNO3 in a single chromatographic separation step

Conditional predictive inference for online surveillance of spatial disease incidence

This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina

Spatiotemporal Infectious Disease Modeling: A BME-SIR Approach

This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the specification of transmission and recovery rates, the model studies the functional formulation of the evolution of the fractions of susceptible-infected-recovered individuals. The suggested approach is capable of: a) modeling population dynamics within and across localities, b) integrating the disease representation (i.e. susceptible-infected-recovered individuals) with observation time series at different geographical locations and other sources of information (e.g. hard and soft data, empirical relationships, secondary information), and c) generating predictions of disease spread and associated parameters in real time, while considering model and observation uncertainties. Key aspects of the proposed approach are illustrated by means of simulations (i.e. synthetic studies), and a real-world application using hand-foot-mouth disease (HFMD) data from China.J.M. Angulo and A.E. Madrid have been partially supported by grants MTM2009-13250 and MTM2012-32666 of SGPI, and P08-FQM-3834 of the Andalusian CICE, Spain. H-L Yu has been partially supported by a grant from National Science Council of Taiwan (NSC101-2628-E-002-017-MY3 and NSC102-2221-E-002-140-MY3). A. Kolovos was supported by SpaceTimeWorks, LLC. G. Christakos was supported by a Yongqian Chair Professorship (Zhejiang University, China)

Spatial heterogeneity in Bayesian disease mapping

© 2018, The Author(s). Disease mapping applications generally assume homogeneous regression effects and use random intercepts to account for residual spatial dependence. However, there may be local variation in the association between disease and area risk factors. We consider implications for model fit, estimated regression coefficients, and substantive inferences of allowing spatial variability in impacts of area risk factors. An application to suicide in 6791 English small areas shows that average regression coefficients and substantive inferences (e.g. about relative risk) may be considerably affected by allowing spatially varying predictor effects, while fit is improved

From spatial ecology to spatial epidemiology: Modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices

Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. Results: PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165thPCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r 2= 0.579). Conclusions: PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information