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Cutting tool parameters and tool grade and geometry were found to have an influence on the performance of the tools.
Further analyses were conducted to explore whether three aspects of reader preference, "preference insight", "preference diversity", and "reading involvement" might influence the performance of the tools.
Results are also given on the relative performance of the tools when analysing several known-good protocols with an increasing number of parallel sessions.
This helps to reduce the friction between the chip and the rake face, which further reduces the cutting force and temperature, and improves the cutting performance of the tools.
The authors find that, in general, the tools work in the sense that they allow users to access pages that are otherwise blocked by filtering countries but that performance of the tools is generally poor and that many tools have significant, unreported security vulnerabilities.
We find that, in general, the tools work in the sense that they allow users to access pages that are otherwise blocked by filtering countries but that performance of the tools is generally poor and that many tools have significant, unreported security vulnerabilities.
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Recommendations are provided for improving the performance of the tool.
The texture pattern orientation of the micro-texture also affects the cutting performance of the tool.
The performance of the tool is evaluated with respect to the aforementioned measurements.
Controlled experiments were conducted to test the performance of the tool setter.
The performance of the tool in generating predictive PLS models is also compared.
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