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Brand romance is considered as a very important construct in marketing especially in brand area leading to the occurrence of some behaviors in consumers. However, a few types of research have been conducted on the importance of brand romance in hoteling. The present research aims to identify and test the effect of brand romance on four kinds of customer loyalty including cognitive loyalty, affective loyalty, conative loyalty and behavioral loyalty. The semi-structured interview was used to identify the measuring tool of brand romance. Then, customer loyalty questionnaire was added and distributed in the statistical society of the study. Data were tested using Smart-Pls2. Results of the research showed that brand romance had positive and significant effect on four kinds of loyalty. This research is a guide for hotel managers and marketers to perceive brand romance from viewpoints of customers. In addition, by applying suggestions, the research will help marketers create a stronger relationship between brand and customers and thus benefit from its outcomes.

Der Wechsel von der Schule in die Arbeitswelt erfordert, in einem erwerbs- und berufsbezogenen Kontext handlungsfähig zu werden. Der Übergang zeigt sich als multikomplexer Prozess, in dem verschiedene Systemvariablen zusammenwirken und unterschiedliche Akteurinnen und Akteure strukturierend Einfluss nehmen. So entstehen Herausforderungen, die sich an der Schnittstelle zwischen der Arbeitswelt, dem Bildungssystem und den Partizipationsmöglichkeiten der Jugendlichen zeigen. Die berufliche Integration kann gefördert werden, wenn der Berufsfindungsprozess durch ein gezieltes Matching unterstützt wird, wenn die Ausbildungsbereitschaft und -fähigkeit der Lehrbetriebe sowie integrative Schulmodelle gefördert und Massnahmen der Supported Education bedarfsorientiert eingesetzt werden.

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Abstract The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence from the data at hand. Several teams used hierarchical clustering (HC) or principal component analysis (PCA) to identify groups of differentially expressed genes with a similar expression pattern over time points and infective agent (E. coli or S. aureus). The main result from these analyses was that HC and PCA were able to separate tissue samples taken at 24 h following E. coli infection from the other samples. The second approach identified groups of differentially co-expressed genes, by identifying clusters of genes highly correlated when animals were infected with E. coli but not correlated more than expected by chance when the infective pathogen was S. aureus. The third approach looked at differential expression of predefined gene sets. Gene sets were defined based on information retrieved from biological databases such as Gene Ontology. Based on these annotation sources the teams used either the GlobalTest or the Fisher exact test to identify differentially expressed gene sets. The main result from these analyses was that gene sets involved in immune defence responses were differentially expressed.

Abstract Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.

Abstract A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE (European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

Abstract Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from 10 countries performed and discussed the statistical analyses of real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays from a direct comparison of two treatments (dye-balanced). While there was broader agreement with regards to methods of microarray normalisation and significance testing, there were major differences with regards to quality control. The quality control approaches varied from none, through using statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful in facilitating interaction between scientists with a diverse background but a common interest in microarray analyses.

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Gemeinsam für Menschen in Not: Das Bündnis „Entwicklung Hilft" macht unter diesem Motto auf die dramatische Lage der Flüchtlinge aufmerksam. Im Rahmen ihrer Kampagne #Grenzgänger reisten Hannah und Felix (beide 22) im September mit Flüchtlingen nach Deutschland. Ihr Weg führte sie von Griechenland über Mazedonien, Serbien, Ungarn und Osterreich. Ihre Tagebuchnotizen geben ein Bild, was sie an den offiziellen und inoffiziellen Grenzübergängen erlebten. [ABSTRACT FROM AUTHOR]