A similar linear dependency of the temperature increase on the input energy has been observed previously in polymer capsules with gold nanoparticles in optical heating systems.
Scale-free networks may have the relative independence from input conditions that has been observed in real biological systems (e.g. [ 22]), and which may be necessary to achieve a stable phenotype.
Three different input power levels (500 W, 1 KW & 1.6 KW) are applied to the agglomerates and it has been observed that higher input powers raise the temperature of the agglomerates very quickly, hence reducing the required residence time of the sample.
A maximum output of 0.8 mJ/(sr 2% bw) at 13.5 nm has been observed with an input pulse energy of 2 J. Operation at a repetition rate of 1 kHz and an electrical input power of 2 kW has been demonstrated with an average emitted power of around 0.3 W/(sr 2% bw).
Through the development of this toolbox, it has been observed that the input parameters for these tools affect the overall efficiency significantly.
When this multiplicative scaling is applied to the input stage, it results in a stereotypical rightward shift of the logarithmic input versus output function of the neuron that has been observed in a wide variety of experiments (25, 27, 37– 41).
This input-dependent phase locking and phase precession has been observed experimentally in nearly all parts of the brain involved in learning [6], [25], [26], and specifically during hippocampal place cell firing [16], [17] when the animal is traversing the place field associated with that cell.
It has been observed that, for higher heat input rates stratification occurs in a shorter time period.
For example, it has been observed that for the same input data, ten different ontological analysis programs resulted in P values ranging several orders of magnitude for some GO terms [7]; the same effect was observed in our study.
