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In a narrow sense, data-intensive applications commonly require significant storage space and intensive computing power.
Solutions based on FPGA-like logic required significant storage for their configuration data and were expensive in area cost and slow to reconfigure (or would require even more area to be faster).
Our benchmarking reveals that shared storage use requires significant time and proves the value of a multiple shared storage node configuration.
Today, materials and system design for novel energy conversion and storage applications requires significant attention towards interfaces between different materials components, as very often these interfaces are rate-limiting for energy transfer, and consequently limiting the overall energy conversion efficiency.
Higher autonomy levels require additional electricity from a CHP engine, while a 100% autonomous system is achievable but requires significant amounts of thermal and electrical storage.
This storage and distribution step requires significant time and resources and may not even be possible in an online/distance learning scenario due to the data sizes involved.
This requires significant gain.
The deck requires significant landscaping.
It requires significant personnel resources.
RPA requires significant IT support.
Digital transformation requires significant cultural change.
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CEO of Professional Science Editing for Scientists @ prosciediting.com