Recently, several types of extensions of the latent class (LC) model have been developed for the analysis of data sets having a multilevel structure. The most popular variant is the multilevel LC model with finite mix...
Recently, several types of extensions of the latent class (LC) model have been developed for the analysis of data sets having a multilevel structure. The most popular variant is the multilevel LC model with finite mixture distributions at multiple levels of a hierarchical structure; that is, with LCs for both lower-level units (e.g. individuals, citizens, or patients) and higher-level units (e.g. groups, regions, or hospitals). A problem in the application of this model is that determining the number of LCs is much more complicated than in standard (single-level) LC analysis because it involves multiple, nonindependent decisions. We propose a three-step model-fitting procedure for deciding about the number of higher- and lower-level classes. We also investigate the performance of information criteria (BIC, AIC, CAIC, and AIC3) in the context of multilevel LC analysis, with different types of response variables. A specific difficulty associated with using BIC and CAIC in any type of multilevel analysis is that these measures contain the sample size in their formulae, and we investigate whether this should be the number of groups, the number of individuals, or either the number of groups or individuals depending on whether one has to decide about model features concerning the higher or lower level. The three main conclusions of our simulations studies are that (1) the proposed three-step model-fitting strategy works rather well, (2) the number of higher-level units (K) is the preferred sample size for BIC and CAIC, both for decisions about higher- and lower-level classes, and (3) with categorical indicators, AIC3 and BIC based on the higher-level sample size are the preferred measures for deciding about the number of LCs at both the higher and lower level. With continuous indicators, BIC(K) performs better than AIC3. AIC performs best in very specific situations—namely, with poorly separated classes and categorical indicators.
This article discusses the issue whether European law permits framework decisions and directives to be transposed into prosecution policy guidelines, taking the Dutch implementation of the Framework Decision on the st...
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There are few studies on the shape and structure of the channel-type induction heating tundish on multi-physics field. Computational fluid dynamics has been used to study the influence of the structure of the tundish ...
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There are few studies on the shape and structure of the channel-type induction heating tundish on multi-physics field. Computational fluid dynamics has been used to study the influence of the structure of the tundish on the macroscopic transport behavior of the tundish with channel induction heating. The results show that increasing the depth of the molten pool is conducive to dynamic behavior of multiphase, the deeper the molten pool, the larger the active area, the longer residence time, the more inclusions removal and the higher ratio of plug to dead volume. Meanwhile, the larger the channel diameter, the more inclusions removal in the receiving chamber and channel. The channel induction heating has enough ability to increase the superheat and temperature compensate for the heat loss caused by the excessive residence time of the molten steel in the tundish. The change in the channel structure is crucial to the macroscopic transport behavior of the fluid. The change in channel diameter has the greatest effect on the multi-physics field in the molten pool.
Objective: Previous studies have used acetylcholinesterase (AChE) histochemistry to identify cholinergic nerves in the heart, but this enzyme is not a selective marker for cholinergic neurons. This study maps choliner...
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Objective: Previous studies have used acetylcholinesterase (AChE) histochemistry to identify cholinergic nerves in the heart, but this enzyme is not a selective marker for cholinergic neurons. This study maps cholinergic innervation of guinea pig heart using a new antibody to the human high-affinity choline transporter (CHT), which is present only in cholinergic nerves. Methods: Immunohistochemistry was used to localize CHTs in frozen and paraffin sections of heart and whole mount preparations of atrial ganglionated nerve plexus. AChE-positive nerve fibers were identified in sections from separate hearts for comparison. Results: Control experiments established that the antibody to human CHT selectively labeled cholinergic neurons in the guinea pig. CHT-immunoreactive nerve fibers and AChE-positive nerves were very abundant in the sinus and AV nodes, bundle of His, and bundle branches. Both markers also delineated a distinct nerve tract in the posterior wall of the right atrium. AChE-positive nerve fibers were more abundant than CHT-immunoreactive nerves in working atrial and ventricular myocardium. CHT-immunoreactive nerves were rarely observed in left ventricular free wall. Both markers were associated with numerous parasympathetic ganglia that were distributed along the posterior atrial walls and within the interatrial septum, including the region of the AV node. Conclusions: Comparison of labeling patterns for CHT and AChE suggests that AChE histochemistry overestimates the density of cholinergic innervation in the heart. The distribution of CHT-immunoreactive nerve fibers and parasympathetic ganglia in the guinea pig heart suggests that heart rate, conduction velocity, and automaticity are precisely regulated by cholinergic innervation. In contrast, the paucity of CHT-immunoreactive nerve fibers in left ventricular myocardium implies that vagal efferent input has little or no direct influence on ventricular contractile function in the guinea pig. (C) 2004 Europe
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