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The wide use of EISs and Auto-ID devices within an enterprise increases the difficulties in data interoperability among difficult data sources.
In particular, the sizes of these data warehouses are forever growing and becoming overloaded with data, a scenario that leads to difficulties in data maintenance and data analysis.
Despite their relevance, many mid-size or small distribution companies have problems to install one of these solutions because they are either very expensive, incompatible, or limited by difficulties in data exchange.
Data regarding both drug-related and non-drug-related costs in patients with Parkinson's disease (PD) are scarce, mainly due to the difficulties in data acquisition in experimental designs.
ANN belongs to a new kind of artificial intelligence technique, which can surmount difficulties in data analysis and model development, so it is suitable for non-linear and unstructured information processing in materials research.
Modeling approaches can provide new insights into the dynamics of plant topology, but they are often hampered by experimental difficulties in data collection on complex trees with numerous internodes, especially in tropical tree species that present the additional difficulty of continuous growth with no marked cessation.
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Furthermore, the difficulty in data interchanges among trading partners can be alleviated.
In general, a hyperspectral image contains hundreds of bands with high spectral resolution, which brings about difficulty in data processing due to data redundancy and complexity.
However, its application is limited mainly due to high cost and difficulty in data processing [ 20].
First, a major limitation of moving into 3D combinatorial encoding approaches is the difficulty in data analysis.
Current paper-based evaluation instruments have numerous limitations: difficulty in data analysis, significant delays in identifying problem trends and poor user compliance.
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