Ecological panel inference in repeated cross sections

This paper presents a Markov chain model for the estimation of individual-level binary transitions from a time series of independent repeated cross-sectional (RCS) samples. Although RCS samples lack direct information on individual turnover, it is demonstrated here that it is possible with these data to draw meaningful conclusions on individual state-to-state transitions. We discuss estimation and inference using maximum likelihood, parametric bootstrap and Markov chain Monte Carlo approaches. The model is illustrated by an application to the rise in ownership of computers in Dutch households since 1986, using a 13-wave annual panel data set. These data encompass more information than we need to estimate the model, but this additional information allows us to assess the validity of the parameter estimates. We examine the determinants of the transitions from 'have-not' to 'have' (and back again) using well-known socio-economic and demographic covariates of the digital divide. Parametric bootstrap and Bayesian simulation are used to evaluate the accuracy and the precision of the ML estimates and the results are also compared with those of a first-order dynamic panel model. To mimic genuine repeated cross-sectional data, we additionally analyse samples of independent observations randomly drawn from the panel. Software implementing the model is available.

Inferring transition probabilities from repeated cross sections: a cross-level inference approach to US presidential voting

This paper outlines a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro-level from a time series of independent cross-sectional samples with a binary outcomevariable. The model has its origins in the work of Moffitt (1993) and shares features with standard statistical methods for ecological inference. We show how ML estimates of the parameters can be obtained by the method-of-scoring, how to estimate time-varying covariate effects, and how to include non-backcastable variables in the model. The latter extension of the basic model is an important one as it strongly increases its potential application in a wide array of research contexts. The example illustration uses survey data on American presidential vote intentions from a five-wavepanel study conducted by Patterson (1980) in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with the observations in the panel. Directions for future work are discussed.Markov model;transition probabilities

Fabrication of Bone Substitute Material by Rapid Prototyping

Bone tissue engineering has gained much attention in recent years. A key requirement in this field is the development of scaffold structures, on which cells adhere. This can be done by fabricating scaffolds by direct procedures like 3D-printing or by indirect procedures like casting. With the 3D-printing process different structures were build up by using hydroxyapatite powder (HA) and a special binder material. Afterwards the printed 3D structures were sintered. For the casting process molds have been made of different resins by stereolithography and other processes using polymers and waxes. These structures were filled by a suspension of HA. By heating the resulting polymer/ceramic composite to a specific temperature it is possible to combust the polymer or wax. By further heating the remaining body, the HA is sintered. Compared to the 3D printing a better resolution can be obtained here. But there are restrictions regarding the ratio of polymer and the HA ceramic during the heating process which means a limitation for the level of porosity.Mechanical Engineerin

Case studies: the environmental impact of DSM projects

Abstract: The critical electricity supply situation in South Africa has brought about the implementation of DSM projects in various industries including the gold mines. However, in certain cases, this may have a negative impact on the environment. As a result of this problem a need to maximise load shifting results with minimal environmental impact has become imperative. This paper presents a study of the possible impact on the environment when attempting load shifting

In-Situ Investigation of Gas Phase Radical Chemistry in the Catalytic Partial Oxidation of Methane on Pt

The catalytic partial oxidation of methane on platinum was studied in situ under atmospheric pressure and temperatures between 1000 and 1300 °C. By combining radical measurements using a molecular beam mass spectrometer and threshold ionization with GC, GC-MS and temperature profile measurements it was demonstrated that a homogeneous reaction pathway is opened at temperatures above 1100 °C, in parallel to hetero-geneous reactions which start already at 600 °C. Before ignition of gas phase chemistry, only CO, H2, CO2 and H2O are formed at the catalyst surface. Upon ignition of gas chemistry, CH3⋅ radicals, C2 coupling products and traces of C3 and C4 hydrocarbons are observed. Because the formation of CH3⋅ radicals correlates with the formation of C2 products it can be concluded that C2 products are formed by coupling of methyl radicals in the gas phase followed by dehydrogenation reactions. This formation pathway was predicted by numerical simulations and this work presents an experimental confirmation under high temperature atmospheric pressure conditions

Significant Conditions on the Two-electron Reduced Density Matrix from the Constructive Solution of N-representability

We recently presented a constructive solution to the N-representability problem of the two-electron reduced density matrix (2-RDM)---a systematic approach to constructing complete conditions to ensure that the 2-RDM represents a realistic N-electron quantum system [D. A. Mazziotti, Phys. Rev. Lett. 108, 263002 (2012)]. In this paper we provide additional details and derive further N-representability conditions on the 2-RDM that follow from the constructive solution. The resulting conditions can be classified into a hierarchy of constraints, known as the (2,q)-positivity conditions where the q indicates their derivation from the nonnegativity of q-body operators. In addition to the known T1 and T2 conditions, we derive a new class of (2,3)-positivity conditions. We also derive 3 classes of (2,4)-positivity conditions, 6 classes of (2,5)-positivity conditions, and 24 classes of (2,6)-positivity conditions. The constraints obtained can be divided into two general types: (i) lifting conditions, that is conditions which arise from lifting lower (2,q)-positivity conditions to higher (2,q+1)-positivity conditions and (ii) pure conditions, that is conditions which cannot be derived from a simple lifting of the lower conditions. All of the lifting conditions and the pure (2,q)-positivity conditions for q>3 require tensor decompositions of the coefficients in the model Hamiltonians. Subsets of the new N-representability conditions can be employed with the previously known conditions to achieve polynomially scaling calculations of ground-state energies and 2-RDMs of many-electron quantum systems even in the presence of strong electron correlation

Inferring transition probabilities from repeated cross sections: a cross-level inference approach to US presidential voting

This paper outlines a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro-level from a time series of independent cross-sectional samples with a binary outcome variable. The model has its origins in the work of Moffitt (1993) and shares features with standard statistical methods for ecological inference. We show how ML estimates of the parameters can be obtained by the method-of- scoring, how to estimate time-varying covariate effects, and how to include non-backcastable variables in the model. The latter extension of the basic model is an important one as it strongly increases its potential application in a wide array of research contexts. The example illustration uses survey data on American presidential vote intentions from a five-wave panel study conducted by Patterson (1980) in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with the observations in the panel. Directions for future work are discussed

Ecological panel inference in repeated cross sections

This paper presents a Markov chain model for the estimation of individual-level binary transitions from a time series of independent repeated cross-sectional (RCS) samples. Although RCS samples lack direct information on individual turnover, it is demonstrated here that it is possible with these data to draw meaningful conclusions on individual state-to-state transitions. We discuss estimation and inference using maximum likelihood, parametric bootstrap and Markov chain Monte Carlo approaches. The model is illustrated by an application to the rise in ownership of computers in Dutch households since 1986, using a 13-wave annual panel data set. These data encompass more information than we need to estimate the model, but this additional information allows us to assess the validity of the parameter estimates. We examine the determinants of the transitions from 'have-not' to 'have' (and back again) using well-known socio-economic and demographic covariates of the digital divide. Parametric bootstrap and Bayesian simulation are used to evaluate the accuracy and the precision of the ML estimates and the results are also compared with those of a first-order dynamic panel model. To mimic genuine repeated cross-sectional data, we additionally analyse samples of independent observations randomly drawn from the panel. Software implementing the model is available

Role of dispersion of vanadia on SBA-15 in the oxidative dehydrogenation of propane

A series of vanadia catalysts supported on the mesoporous silica SBA-15 are synthesized using an automated laboratory reactor. The catalysts contain from 0.6 up to 13.6V atoms/nm2 and are structurally characterized by various techniques (BET, XRD, SEM, TEM, Raman, IR, UV/Vis). Samples containing up to 3.1V/nm2 are structurally rather similar. They all contain a mixture of tetrahedral (VOx)n species, both monomeric and oligomeric. The ratio of monomeric and oligomeric species depends on the vanadia loading. At the highest loading of 13.6V/nm2, in addition to tetrahedral (VOx)n, also substantial amounts of three-dimensional, bulk-like V2O5 are present in the catalyst. The structural similarity of the low-loaded catalysts is reflected in their alike catalytical activity during the oxidative dehydrogenation (ODH) of propane between 380 and 480 °C. Propene, CO, and CO2 are formed as reaction products, while neither the formation of ethene nor acrolein or acrylic acid is observed in other than trace amounts. The activation energy for ODH of propane is not, vert, similar140 kJ/mol. The catalyst with the highest loading yields varying activation energies for different reaction conditions, which is probably related to rearrangements between bulk-like and dispersed, two-dimensional (VOx)n. Rather than the monomer to oligomer ratio, the ratio of two-dimensional to three-dimensional vanadia seems to be crucial for the catalytic properties of silica supported vanadia in the ODH of propane

Dynamical N-body Equlibrium in Circular Dilaton Gravity

We obtain a new exact equilibrium solution to the N-body problem in a one-dimensional relativistic self-gravitating system. It corresponds to an expanding/contracting spacetime of a circle with N bodies at equal proper separations from one another around the circle. Our methods are straightforwardly generalizable to other dilatonic theories of gravity, and provide a new class of solutions to further the study of (relativistic) one-dimensional self-gravitating systems.Comment: 4 pages, latex, reference added, minor changes in wordin