/**
* Called when a null model is about to be retrieved in order to allow a subclass to provide an
* initial model.
* <p>
* By default this implementation looks components in the parent chain owning a
* {@link IComponentInheritedModel} to provide a model for this component via
* {@link IComponentInheritedModel#wrapOnInheritance(Component)}.
* <p>
* For example a {@link FormComponent} has the opportunity to instantiate a model on the fly
* using its {@code id} and the containing {@link Form}'s model, if the form holds a
* {@link CompoundPropertyModel}.
*
* @return The model
*/
protected IModel<?> initModel()
{
IModel<?> foundModel = null;
// Search parents for IComponentInheritedModel (i.e. CompoundPropertyModel)
for (Component current = getParent(); current != null; current = current.getParent())
{
// Get model
// Don't call the getModel() that could initialize many in between
// completely useless models.
// IModel model = current.getDefaultModel();
IModel<?> model = current.getModelImpl();
if (model instanceof IWrapModel && !(model instanceof IComponentInheritedModel))
{
model = ((IWrapModel<?>)model).getWrappedModel();
}
if (model instanceof IComponentInheritedModel)
{
// return the shared inherited
foundModel = ((IComponentInheritedModel<?>)model).wrapOnInheritance(this);
setFlag(FLAG_INHERITABLE_MODEL, true);
break;
}
}
// No model for this component!
return foundModel;
}
Guided by the risk information-seeking and processing model, this study examines positive and negative affect separately in their influence on information-seeking intentions and avoidance through structural equation analyses. The highlight is that information avoidance seems to be driven by positive affect, while information seeking seems to be more heavily influenced by negative affect. Another interesting finding is that informational subjective norms are positively related to both seeking and avoidance, which suggests that one’s social environment has the potential to strongly influence the way he or she handles climate change information. Implications for theory and practice are discussed.
Welcome to the world of evidence! Evidence-based teaching is effective teaching, and we bring you the most effective methods. Read on to find out more.
A. Garc\'ıa Frey, G. Calvary, und S. Dupuy-Chessa. Proceedings of the 2Nd ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Seite 41--46. New York, NY, USA, ACM, (2010)
R. von Behren, J. Condit, und E. Brewer. Proceedings of the 9th conference on Hot Topics in Operating Systems - Volume 9, Seite 4. Berkeley, CA, USA, USENIX Association, (2003)
P. Xia, S. Wu, und B. Van Durme. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Seite 7516--7533. Association for Computational Linguistics, (November 2020)
A. Monreale, F. Pinelli, R. Trasarti, und F. Giannotti. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 637-646. ACM, (2009)
J. Hausmann, und S. Kent. SoftVis '03: Proceedings of the 2003 ACM symposium on Software visualization, Seite 169--178. New York, NY, USA, ACM Press, (2003)
J. Hausmann, und S. Kent. SoftVis '03: Proceedings of the 2003 ACM symposium on Software visualization, Seite 169--178. New York, NY, USA, ACM Press, (2003)
I. Cadez, D. Heckerman, C. Meek, P. Smyth, und S. White. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 280--284. ACM, (2000)
L. Wu, M. Li, Z. Li, W. Ma, und N. Yu. MIR '07: Proceedings of the international workshop on Workshop on multimedia information retrieval, Seite 115--124. New York, NY, USA, ACM, (2007)
A. Shaikh, R. Clarisó, U. Wiil, und N. Memon. Proceedings of the IEEE/ACM international conference on Automated software engineering, Seite 185--194. New York, NY, USA, ACM, (2010)
P. Carpenter. Ada Lett., XIX (3):
23--29(1999)ST: Vorgehensweise: Das Paper ordnet den Vorgang, wie man sicherheitskritische Anforderungen verifizieren kann, in einen Software Life-Cycle ein. Use-Cases werden mit Parametern für Daten versehen. Die Eingabedaten werden mit Hilfe eines Tools generiert per üblicher Ä-Klassenanalyse.
Eignung: Es ist nichts über die Testgüte zu finden (Abdeckungskriterium etc.). Außerdem wird kein Testmodell o.ä. erwähnt, welches alternative Ausführungspfade des Use Cases repräsentiert..