F-transforms for the definition of contextual fuzzy partitions

Fuzzy partitions can be defined in many different ways, but usually, it is done taking into account the whole universe. In this paper, we present a method to define fuzzy partitions according to those elements in the universe holding certain fuzzy attribute. Specifically, we show how to define those fuzzy partitions by means of F-transforms.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech This work has been partially supported by the Spanish Ministry of Science by the projects TIN15-70266-C2-P-1 and TIN2016-76653-

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

Same-Sex Parents Negotiating the Law in Italy: Between Claims of Recognition and Practices of Exclusion

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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

An Introduction to Migration and Transnationalism Between Switzerland and Bulgaria

Since the fall of the Iron Curtain, the political order of the borders within Europe has changed and has, subsequently, led to new paths and forms of migration. By discussing the case of Bulgarian–Swiss migration, this book gives an insight into such new patterns of migration. This chapter provides an introduction to the debates on East–West migration in Europe and situates the case we discuss within these debates. Further, it outlines the concepts that theoretically frame the analysis developed throughout the book: Transnationalism is used as an umbrella concept to capture practices and networks across countries but also to open up the term migration to include other forms of mobility such as circular movements. Social inequalities are often seen as a driver for migration; furthermore, they also structure migration patterns and are, vice versa, also affected by migration. Regional disparities provide the background to analyse the origin of migrants within a country and couple it with questions such as the economic development of certain regions. Finally, policies provide the context that frames and structures migration patterns. The chapter then outlines the empirical basis for the research including quantitative as well as qualitative data. Lastly, the chapter concludes with an overview of the various chapters of the book

Between Return and Circulation: Experiences of Bulgarian Migrants

This article assesses the extent and specifics of return and circular migration in Bulgaria, a South-eastern European country that joined the European Union in 2007. After defining return migration and reviewing the main theories contemplating return (and circular) migration, the principal section of the article deals with the current Bulgarian migration scenario. It draws from quantitative research data as well as insights from semi-structured interviews carried out in the country in summer 2014 for the research project “Migration and Transnationalism Between Switzerland and Bulgaria”. At the end, a typical biography of a return and circular migrant is presented and compared