Modelling of the radiative properties of an opaque porous ceramic layer

Solid Oxide Fuel Cells (SOFCs) operate at temperatures above 1,100 K where radiation effects can be significant. Therefore, an accurate thermal model of an SOFC requires the inclusion of the contribution of thermal radiation. This implies that the thermal radiative properties of the oxide ceramics used in the design of SOFCs must be known. However, little information can be found in the literature concerning their operating temperatures. On the other hand, several types of ceramics with different chemical compositions and microstructures for designing efficient cells are now being tested. This is a situation where the use of a numerical tool making possible the prediction of the thermal radiative properties of SOFC materials, whatever their chemical composition and microstructure are, may be a decisive help. Using this method, first attempts to predict the radiative properties of a lanthanum nickelate porous layer deposited onto an yttria stabilized zirconium substrate can be reported

Academic careers in global pulmonary and critical care medicine

The burden of respiratory and critical illness is high worldwide, yet specialist care is underrepresented in low- and middle-income countries (LMICs) [1]. For many areas of medicine, the past decade has witnessed tremendous growth in global health opportunities for trainees; however, these opportunities tend to be restricted to individual institutions and geographic regions and academic global pulmonary and critical care medicine (PCCM) remains a relatively novel concept [2]. Consequently, PCCM fellows and junior faculty at institutions with limited global health mentorship have little guidance in building successful global health careers

Power-Law Scaling in the Brain Surface Electric Potential

Recent studies have identified broadband phenomena in the electric potentials produced by the brain. We report the finding of power-law scaling in these signals using subdural electrocorticographic recordings from the surface of human cortex. The power spectral density (PSD) of the electric potential has the power-law form from 80 to 500 Hz. This scaling index, , is conserved across subjects, area in the cortex, and local neural activity levels. The shape of the PSD does not change with increases in local cortical activity, but the amplitude, , increases. We observe a “knee” in the spectra at , implying the existence of a characteristic time scale . Below , we explore two-power-law forms of the PSD, and demonstrate that there are activity-related fluctuations in the amplitude of a power-law process lying beneath the rhythms. Finally, we illustrate through simulation how, small-scale, simplified neuronal models could lead to these power-law observations. This suggests a new paradigm of non-oscillatory “asynchronous,” scale-free, changes in cortical potentials, corresponding to changes in mean population-averaged firing rate, to complement the prevalent “synchronous” rhythm-based paradigm

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Social sciences research in neglected tropical diseases 2: A bibliographic analysis

The official published version of the article can be found at the link below.Background There are strong arguments for social science and interdisciplinary research in the neglected tropical diseases. These diseases represent a rich and dynamic interplay between vector, host, and pathogen which occurs within social, physical and biological contexts. The overwhelming sense, however, is that neglected tropical diseases research is a biomedical endeavour largely excluding the social sciences. The purpose of this review is to provide a baseline for discussing the quantum and nature of the science that is being conducted, and the extent to which the social sciences are a part of that. Methods A bibliographic analysis was conducted of neglected tropical diseases related research papers published over the past 10 years in biomedical and social sciences. The analysis had textual and bibliometric facets, and focussed on chikungunya, dengue, visceral leishmaniasis, and onchocerciasis. Results There is substantial variation in the number of publications associated with each disease. The proportion of the research that is social science based appears remarkably consistent (<4%). A textual analysis, however, reveals a degree of misclassification by the abstracting service where a surprising proportion of the "social sciences" research was pure clinical research. Much of the social sciences research also tends to be "hand maiden" research focused on the implementation of biomedical solutions. Conclusion There is little evidence that scientists pay any attention to the complex social, cultural, biological, and environmental dynamic involved in human pathogenesis. There is little investigator driven social science and a poor presence of interdisciplinary science. The research needs more sophisticated funders and priority setters who are not beguiled by uncritical biomedical promises

Synapse Clusters Are Preferentially Formed by Synapses with Large Recycling Pool Sizes

Synapses are distributed heterogeneously in neural networks. The relationship between the spatial arrangement of synapses and an individual synapse's structural and functional features remains to be elucidated. Here, we examined the influence of the number of adjacent synapses on individual synaptic recycling pool sizes. When measuring the discharge of the styryl dye FM1–43 from electrically stimulated synapses in rat hippocampal tissue cultures, a strong positive correlation between the number of neighbouring synapses and recycling vesicle pool sizes was observed. Accordingly, vesicle-rich synapses were found to preferentially reside next to neighbours with large recycling pool sizes. Although these synapses with large recycling pool sizes were rare, they were densely arranged and thus exhibited a high amount of release per volume. To consolidate these findings, functional terminals were marked by live-cell antibody staining with anti-synaptotagmin-1-cypHer or overexpression of synaptopHluorin. Analysis of synapse distributions in these systems confirmed the results obtained with FM 1–43. Our findings support the idea that clustering of synapses with large recycling pool sizes is a distinct developmental feature of newly formed neural networks and may contribute to functional plasticity

Chinese Americans’ Views and Use of Family Health History: A Qualitative Study

Objective Family health history (FHH) plays a significant role in early disease detection and preven- tion. Although Asian Americans are the fastest growing U.S. immigrant group, no data exists regarding Chinese Americans’ (the largest Asian subgroup) views and use of FHH. This study examines this important issue. Methods Forty-nine adults from southern U.S. Chinese American communities participated in this qualitative, semi-structured, in-depth interview study. Interviews were audio recorded, tran- scribed, and analyzed with a content analysis approach. Results Although the majority of participants perceived the importance of collecting FHH, most lacked FHH knowledge and failed to collect FHH information. Barriers affecting FHH collec- tion and discussion among family members included long-distance separation from family members, self-defined “healthy family,� and Chinese cultural beliefs. Lack of doctors’ inqui- ries, never/rarely visiting physicians, self-defined “healthy family,� perceived insignificance of discussing FHH with doctors, and Chinese cultural beliefs were the obstacles in commu- nicating FHH with physicians. Conclusions Chinese Americans had limited usage of their FHH and faced cultural, distance, knowl- edge-, and healthcare system-related barriers that influenced their FHH use. Developing FHH education programs for Chinese Americans is highly recommended

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction