Distribution of current density across the active area at various stoichiometry ratios using the JRC ZERO∇CELL single cell PEM fuel cell testing hardware

The performance of the PEM fuel cell directly depends on the partial pressure of provided reactants, namely hydrogen and oxygen. Since reactants are consumed in the fuel cell reaction, partial pressure of reactants decreases in the direction of reactants flow. This well-known mechanism makes the performance of the fuel cell dependent on the stoichiometry ratios of input reactants. The JRC ZERO∇CELL, a single cell PEM fuel cell testing setup, is developed to provide as much as possible uniform operating conditions at the 10cm2 active area specimen, hence giving uniform current density across the active area of the cell. To investigate what is the real gradient of current density across the active area for the JRC ZERO∇CELL at various reactant stoichiometry ratios, segmented bi-polar plates and current collectors are developed. This study presents experimental investigation of the current density distribution across the active area of the JRC ZERO∇CELL setup at range of reactant stoichiometry ratios from λ = 2 up to λ = 15. Current density gradients are considered along the gas flow as well as in the transverse direction. The experimental results show that the current density gradient across the active area, although dependant on the reactants stoichiometry ratios, is relatively small as compared with a wide range of investigated stoichiometry ratios.

USP: an independence test that improves on Pearson’s chi-squared and the G -test

We present the U -statistic permutation (USP) test of independence in the context of discrete data displayed in a contingency table. Either Pearson’s χ 2 -test of independence, or the G -test, are typically used for this task, but we argue that these tests have serious deficiencies, both in terms of their inability to control the size of the test, and their power properties. By contrast, the USP test is guaranteed to control the size of the test at the nominal level for all sample sizes, has no issues with small (or zero) cell counts, and is able to detect distributions that violate independence in only a minimal way. The test statistic is derived from a U -statistic estimator of a natural population measure of dependence, and we prove that this is the unique minimum variance unbiased estimator of this population quantity. The practical utility of the USP test is demonstrated on both simulated data, where its power can be dramatically greater than those of Pearson’s test, the G -test and Fisher’s exact test, and on real data. The USP test is implemented in the R package USP .

Zero-cell corrections in random-effects meta-analyses

The standard estimator for the log odds ratio (the unconditional maximum likelihood estimator) and the delta-method estimator for its standard error are not defined if the corresponding 2x2 table contains at least one "zero cell". This is also an issue when estimating the overall log odds ratio in a meta-analysis. It is well known that correcting for zero cells by adding a small increment should be avoided. Nevertheless, these zero-cell corrections continue to be used. With this article, we want to warn of a particularly bad zero-cell correction. For this, we conduct a simulation study comparing the following two zero-cell corrections under the ordinary random-effects model: (i) adding 1/2 to all cells of all the individual studies' 2x2 tables independently of any zero-cell occurrences and (ii) adding 1/2 to all cells of only those 2x2 tables containing at least one zero cell. The main finding is that correction (i) performs worse than correction (ii). Thus, we strongly discourage the use of correction (i).

Regenerative processes for Poisson zero polytopes

Let (Mt:t>0) be a Markov process of tessellations of ℝℓ, and let (𝒞t:t>0) be the process of their zero cells (zero polytopes), which has the same distribution as the corresponding process for Poisson hyperplane tessellations. In the present paper we describe the stationary zero cell process (at𝒞at:t∈ℝ),a>1, in terms of some regenerative structure and we show that it is a Bernoulli flow. An important application is to STIT tessellation processes.

Silent pituitary adenomas: review and clinical cases

Silent, or clinically nonfunctioning adenomas are morphologically heterogeneous group, characterized by positive immunoreactivity for one or more hormones classically secreted by normal pituitary cells but without clinical expression. Although in some occasions enhanced or changed secretory activity can develop over time. According to immunoreactivity they are divided into "silent" gonado-, cortico-, somato -, mammo – and thyrotropinomas, oncocytomas, «zero-cell» tumors. All types of "silent" adenomas have different biological activity, secretory capacity and outcomes in the postoperative period. This series of clinical cases shows more «aggressiveness», a higher risk of relapse for "silent" cortico- and somatotropinomas. Immunohistochemical analysis of residual tissue can be used to identify patients with high risk of recurrence, to develop optimal treatment and follow-up.

Approximation Properties of Random Polytopes Associated with Poisson Hyperplane Processes

We consider a stationary Poisson hyperplane process with given directional distribution and intensity in d-dimensional Euclidean space. Generalizing the zero cell of such a process, we fix a convex body K and consider the intersection of all closed halfspaces bounded by hyperplanes of the process and containing K. We study how well these random polytopes approximate K (measured by the Hausdorff distance) if the intensity increases, and how this approximation depends on the directional distribution in relation to properties of K.

Approximation Properties of Random Polytopes Associated with Poisson Hyperplane Processes

We consider a stationary Poisson hyperplane process with given directional distribution and intensity ind-dimensional Euclidean space. Generalizing the zero cell of such a process, we fix a convex bodyKand consider the intersection of all closed halfspaces bounded by hyperplanes of the process and containingK. We study how well these random polytopes approximateK(measured by the Hausdorff distance) if the intensity increases, and how this approximation depends on the directional distribution in relation to properties ofK.