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Because the heterogeneity of historical masonry arch bridges, the analysis and interpretation of GPR data can be complex.
Monitoring the vast amount of data can be complex, and data filtering and analysis processes are desirable.
Moreover, in high dimensional data spaces these single correlations are noisy and the underlying correlation structure of the data can be complex [10].
Interpretation of the vast amount of multidimensional genomic data can be complex and problematic.
Interpretation of subtype-specific serologic data can be complex, especially in birds that are normally infected with several IAV subtypes during their lives.
Medical data can be complex in nature as well as being interconnected and interdependent; hence simplification of this complexity is important.
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Analyses of the stable hydrogen and oxygen isotope composition of water can be used to estimate evaporative losses from individual pools in such regions in the absence of instrumental data but calculations can be complex, especially in highly variable systems.
However, describing dependencies and control between fine-grained data-flow tasks can be complex and present unwanted overheads.
The uploaded data is usually a list of simple string values, however in some cases it also can be complex data values containing information other than a simple label.
Statistical data derived from these curves can be complex, and a variety of factors may impact interpretation.
This paper investigates the feasibility of using data envelopment analysis (DEA) to measure efficiency and rationalise a distribution network as an alternative approach to the conventional method of optimising delivery routes and schedules through linear programming, which can be complex and data demanding.
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