Since the New Property Rights theory focuses exclusively on the power vested in property rights, it cannot accommodate meta-organizations which are characterized by the absence of formal authority and the non-integration of property rights.
The procedures accommodate changes in the meta data and immediately reflect the changes in the application layer.
Machine learning techniques are more supportive to accommodate structural information e.g. meta-data as non-textual features (Katz et al. 2015).
The use of statistical methods to enhance data extraction and a random-effects meta-analysis to accommodate heterogeneity of ENS therapies permitted an adequate number of well designed trials of ENS to be included in the meta-analysis.
Random-effects meta-analysis models can accommodate unexplained heterogeneity for the available pairwise comparisons and often make the incoherence signals less prominent.
Similarly, the combination of data in meta-analytical frameworks will have to accommodate conceivable differences in both effect size and direction of effect across strata.
Because meta-analytical methods are less equipped to accommodate structural dependence of data within repeated-measures designs (Peters & Mengersen, 2008), the repeated-measures experiment (experiment 3) was treated as between-subjects for the purpose of this meta-analysis.
Some applicants merely describe a plan to deposit the data in some web-based public resource, which may or may not be appropriate, have sufficient resources, have a suitable meta-data schema, include compatible ontologies or accommodate adequate data provenance.
Such differences and others mean that meta-synthesis methods must be developed to accommodate methodological complexity but remain able to be tailored to assist the analytic process.
Hence, in this paper we extend Dung's theory to accommodate arguments that claim preferences between other arguments, thus incorporating meta-level argumentation based reasoning about preferences in the object level.
The current approach for evaluating the risk of random error in meta-analyses (MAs) using trial sequential analysis (TSA) can accommodate binary and continuous data but not time-to-event data.
