Assets and domestic units: methodological challenges for longitudinal studies of poverty dynamics

Tracking change in assets access and ownership in longitudinal research is difficult. Assets are rarely assigned to individuals. Their benefit and management are spread across domestic units which morph over time. We review the challenges of using assets to understand poverty dynamics, and tracking the domestic units that own and manage assets. Using case studies from longitudinal research we demonstrate that assets can afford useful insights into important change

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Oral health and obesity indicators

<p>Abstract</p> <p>Background</p> <p>In western Sweden, the aim was to study the associations between oral health variables and total and central adiposity, respectively, and to investigate the influence of socio-economic factors (SES), lifestyle, dental anxiety and co-morbidity.</p> <p>Methods</p> <p>The subjects constituted a randomised sample from the 1992 data collection in the Prospective Population Study of Women in Gothenburg, Sweden (n = 999, 38- > =78 yrs). The study comprised a clinical and radiographic examination, together with a self-administered questionnaire. Obesity was defined as body mass index (BMI) > =30 kg/m², waist-hip ratio (WHR) > =0.80, and waist circumference >0.88 m. Associations were estimated using logistic regression including adjustments for possible confounders.</p> <p>Results</p> <p>The mean BMI value was 25.96 kg/m², the mean WHR 0.83, and the mean waist circumference 0.83 m. The number of teeth, the number of restored teeth, xerostomia, dental visiting habits and self-perceived health were associated with both total and central adiposity, independent of age and SES. For instance, there were statistically significant associations between a small number of teeth (<20) and obesity: BMI (OR 1.95; 95% CI 1.40-2.73), WHR (1.67; 1.28-2.19) and waist circumference (1.94; 1.47-2.55), respectively. The number of carious lesions and masticatory function showed no associations with obesity. The obesity measure was of significance, particularly with regard to behaviour, such as irregular dental visits, with a greater risk associated with BMI (1.83; 1.23-2.71) and waist circumference (1.96; 1.39-2.75), but not with WHR (1.29; 0.90-1.85).</p> <p>Conclusions</p> <p>Associations were found between oral health and obesity. The choice of obesity measure in oral health studies should be carefully considered.</p

A Parallel and Broadband Helmholtz FMBEM Model for Large-Scale Target Strength Modeling

© The Authors. The Fast Multipole Boundary Element Method (FMBEM) reduces the O(N2) computational and memory complexity of the conventional BEM discretized with N boundary unknowns, to O(NlogN) and O(N), respectively. A number of massively parallel FMBEM models have been developed in the last decade or so for CPU, GPU and heterogeneous architectures, which are capable of utilizing hundreds of thousands of CPU cores to treat problems with billions of degrees of freedom (dof). On the opposite end of this spectrum, small-scale parallelization of the FMBEM to run on the typical workstation computers available to many researchers allows for a number of simplifications in the parallelization strategy. In this paper, a novel parallel broadband Helmholtz FMBEM model is presented, which utilizes a simple columnwise distribution scheme, element reordering and rowwise compression of data, to parallelize all stages of the fast multipole method (FMM) algorithm with a minimal communication overhead. The sparse BEM near-field and sparse approximate inverse preconditioner are also constructed and executed in parallel, while the flexible generalized minimum residual (fGMRES) solver has been modified to apply the FMBEM matrix-vector products and corresponding minimum residual convergence within the parallel environment. The algorithmic and memory complexities of the resulting parallel FMBEM model are shown to reaffirm the above estimates for both the serial and parallel configurations. The parallel efficiency (PE) of the FMBEM matrix-vector products and fGMRES solution for the present model is shown to be satisfactory; achieving PEs up to 92.3% and 74.1% in the fGMRES solution using 3 and 6 CPU cores respectively, when applied to models having >104 dof per CPU core. The PE of the precalculation stages of the FMBEM — in particular the FMM precomputation stage which is largely unparallelized — reduces the overall PE of the FMBEM model; resulting in average efficiencies of 68.3% and 47% for the 3-core and 6-core models when treating problems with >104 dof per CPU core. The present model is able to treat large-scale acoustic scattering problems involving up to 107 dof on a workstation computer equipped with 128GB of RAM, while acoustic target strength (TS) results calculated up to 3kHz for the BeTSSi II submarine model demonstrate its capabilities for large-scale TS modeling