Pore-scale modeling of capillary trapping in water-wet porous media: A new cooperative pore-body filling model

We present a pore-network model study of capillary trapping in water-wet porous media. The amount and distribution of trapped non-wetting phase is determined by the competition between two trapping mechanisms - snap-off and cooperative pore-body filling. We develop a new model to describe the pore-body filling mechanism in geologically realistic pore-networks. The model accounts for the geometrical characteristics of the pore, the spatial location of the connecting throats and the local fluid topology at the time of the displacement. We validate the model by comparing computed capillary trapping curves with published data for four different water-wet rocks. Computations are performed on pore-networks extracted from micro-CT images and process-based reconstructions of the actual rocks used in the experiments. Compared with commonly used stochastic models, the new model describes more accurately the experimental measurements, especially for well connected porous systems where trapping is controlled by subtleties of the pore structure. The new model successfully predicts relative permeabilities and residual saturation for Bentheimer sandstone using in-situ measured contact angles as input to the simulations. The simulated trapped cluster size distributions are compared with predictions from percolation theory.The authors wish to acknowledge financial assistance provided through Australian National Low Emissions Coal Research and Development (Grant 7-0311-0128)

Analysis of errors in histology by root cause analysis: a pilot study

Introduction. The study objective is to evaluate critical points in the process of pre-analytical histology in an Anatomic Pathology laboratory. Errors are an integral part of human systems, includ- ing the complex system of Anatomic Pathology. Previous studies focused on errors committed in diagnosis and did not consider the issues related to the histology preparation of routine processes. Methods. Root Cause Analysis was applied to the process of histology preparation in order to identify the root cause of each previously identified problem. The analysis started by defining an ?a priori? list of errors that could occur in the histology prepara- tion processes. During a three-month period, a trained technician tracked the errors encountered during the process and reported them on a form. ?Fishbone? diagram and ?Five whys? methods were then applied. Results. 8,346 histological cases were reviewed, for which 19,774 samples were made and from which 29,956 histologies were pre- pared. 132 errors were identified. Errors were detected in each phase: accessioning (6.5%), gross dissecting (28%), processing (1.5%), embedding (4.5%), tissue cutting and slide mounting (23%), coloring, (1.5%), labeling and releasing (35%). Discussion. Root cause analysis is effective and easy to use in clinical risk management. It is an important step for the identifi- cation and prevention of errors, that are frequently due to multi- ple causes. Developing operators? awareness of their central role in the risk management process is possible by targeted training. Furthermore, by highlighting the most relevant points of interest, it is possible to improve both the methodology and the procedural safety

Transnational reflections on transnational research projects on men, boys and gender relations

This article reflects on the research project, ‘Engaging South African and Finnish youth towards new traditions of non-violence, equality and social well-being’, funded by the Finnish and South African national research councils, in the context of wider debates on research, projects and transnational processes. The project is located within a broader analysis of research projects and projectization (the reduction of research to separate projects), and the increasing tendencies for research to be framed within and as projects, with their own specific temporal and organizational characteristics. This approach is developed further in terms of different understandings of research across borders: international, comparative, multinational and transnational. Special attention is given to differences between research projects that are in the Europe and the EU, and projects that are between the global North and the global South. The theoretical, political and practical challenges of the North-South research project are discussed

Clustering Nominal and Numerical Data: A New Distance Concept for a Hybrid Genetic Algorithm

As intrinsic structures, like the number of clusters, is, for real data, a major issue of the clustering problem, we propose, in this paper, CHyGA (Clustering Hybrid Genetic Algorithm) an hybrid genetic algorithm for clustering. CHyGA treats the clustering problem as an optimization problem and searches for an optimal number of clusters characterized by an optimal distribution of instances into the clusters. CHyGA introduces a new representation of solutions and uses dedicated operators, such as one iteration of K-means as a mutation operator. In order to deal with nominal data, we propose a new definition of the cluster center concept and demonstrate its properties. Experimental results on classical benchmarks are given

Migration, Mobility and Human Rights at the Eastern Border of the European Union - Space of Freedom and Security

This edited collection of migration papers would like to emphasise the acute need for migration related study and research in Romania. At this time, migration and mobility are studied as minor subjects in Economics, Sociology, Political Sciences and European Studies only (mostly at post-graduate level). We consider that Romanian universities need more ‘migration studies’, while research should cover migration as a whole, migration and mobility being analysed from different points of view – social, economical, legal etc. Romania is part of the European Migration Space not only as a source of labourers for the European labour market, but also as source of quality research for the European scientific arena. Even a country located at the eastern border of the European Union, we consider Romania as part of the European area of freedom, security and justice, and therefore interested in solving correctly all challenges incurred by the complex phenomena of migration and workers’ mobility at the European level. The waves of illegal immigrants arriving continuously on the Spanish, Italian and Maltese shores, and the workers’ flows from the new Member States from Central and Eastern Europe following the 2004 accession, forced the EU officials and the whole Europe to open the debate on the economical and mostly social consequences of labour mobility. This study volume is our contribution to this important scientific debate. Starting with the spring of 2005, the Jean Monnet European Centre of Excellence and the School of High Comparative European Studies (SISEC), both within the West University of Timisoara, have proposed a series of events in order to raise the awareness of the Romanian scientific environment on this very sensitive issues: migration and mobility in the widen European Space. An annual international event to celebrate 9 May - The Europe Day was already a tradition for SISEC (an academic formula launched back in 1995 in order to prepare national experts in European affairs, offering academic post-graduate degrees in High European Studies). With the financial support from the Jean Monnet Programme (DG Education and Culture, European Commission), a first migration panel was organised in the framework of the international colloquium ‘Romania and the European Union in 2007’ held in Timisoara between 6 and 7 of May 2005 (panel Migration, Asylum and Human Rights at the Eastern Border of the European Union). Having in mind the positive welcoming of the migration related subjects during the 2005 colloquium, a second event was organised on 5 May 2006 in the framework of the European Year of Workers’ Mobility: the international colloquium Migration and Mobility: Assets and Challenges for the Enlargement of the European Union. In the same period, the Jean Monnet European Centre of Excellence, SISEC and The British Council in Bucharest have jointly edited two special issues of The Romanian Journal of European Studies, no.4/2005 and 5-6/2006, both dedicated to migration and mobility. Preliminary versions of many of the chapters of this volume were presented at the above mentioned international events. The papers were chosen according to their scientific quality, after an anonymously peer-review selection. The authors debate both theoretical issues and practical results of their research. They are renowned experts at international level, members of the academia, PhD students or experienced practitioners involved in the management of the migration flows at the governmental level. This volume was financed by the Jean Monnet Programme of the Directorate General Education and Culture, European Commission, throughout the Jean Monnet European Centre of Excellence (C03/0110) within the West University of Timisoara, Romania, and is dedicated to the European Year of Workers’ Mobility 2006. Timisoara, December 200

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

Formalized Conceptual Spaces with a Geometric Representation of Correlations

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define various operations for our formalization, both for creating new concepts from old ones and for measuring relations between concepts. We present an illustrative toy-example and sketch a research project on concept formation that is based on both our formalization and its implementation.Comment: Published in the edited volume "Conceptual Spaces: Elaborations and Applications". arXiv admin note: text overlap with arXiv:1706.06366, arXiv:1707.02292, arXiv:1707.0516

Baseline factors predictive of serious suicidality at follow-up: findings focussing on age and gender from a community-based study

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-244X/10/41Background: Although often providing more reliable and informative findings relative to other study designs, longitudinal investigations of prevalence and predictors of suicidal behaviour remain uncommon. This paper compares 12-month prevalence rates for suicidal ideation and suicide attempt at baseline and follow-up; identifies new cases and remissions; and assesses the capacity of baseline data to predict serious suicidality at follow-up, focusing on age and gender differences. Methods: 6,666 participants aged 20-29, 40-49 and 60-69 years were drawn from the first (1999-2001) and second (2003-2006) waves of a general population survey. Analyses involved multivariate logistic regression. Results: At follow-up, prevalence of suicidal ideation and suicide attempt had decreased (8.2%-6.1%, and 0.8%-0.5%, respectively). However, over one quarter of those reporting serious suicidality at baseline still experienced it four years later. Females aged 20-29 never married or diagnosed with a physical illness at follow-up were at greater risk of serious suicidality (OR = 4.17, 95% CI = 3.11-5.23; OR = 3.18, 95% CI = 2.09-4.26, respectively). Males aged 40-49 not in the labour force had increased odds of serious suicidality (OR = 4.08, 95% CI = 1.6-6.48) compared to their equivalently-aged and employed counterparts. Depressed/anxious females aged 60-69 were nearly 30% more likely to be seriously suicidal. Conclusions: There are age and gender differentials in the risk factors for suicidality. Life-circumstances contribute substantially to the onset of serious suicidality, in addition to symptoms of depression and anxiety. These findings are particularly pertinent to the development of effective population-based suicide prevention strategies.A Kate Fairweather-Schmidt, Kaarin J Anstey, Agus Salim and Bryan Rodger

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure