Algorithm 2 describes the process of finding maximum s-size k-cliques in the community network.
The results are shown in Fig. 12, where the continuous black lines represent the curves obtained by fitting Neubauer and Rostásy's model (Neubauer and Rostásy 1997) with the experimental data by a minimization process of the maximum loads for the tested samples with different bonded lengths.
Specifically, energies absorbed by the specimen over the entire failure process and values of maximum load occurring during the impact process are measured over a large range of temperatures.
In order to help the operator to optimize the reduction process in terms of maximum amount of mineral extracted and minimum energy consumption, a control system integrated by several modules is being designed.
Successful scale-up of the microwell process (in terms of maximum cell growth rate, biomass yield and specific TK activity) to a 1.4 l scale mechanically stirred bioreactor is then demonstrated based on experiments performed at constant kLa values.
During this process, the principle of maximum entropy, fractional moments and dimension reduction method are used to estimate the failure probability with a good accuracy and a low computational demand.
Whilst it is feasible that algorithm 2 simulates an anisotropic process with direction of maximum autocorrelation that does not follow any of the directions tested here, it is unlikely that such directionality would not be evident in the autocorrelogram difference statistics in either the cardinal or ordinal directions.
However, the activity of endoglucanase, exoglucanase, and β-glucosidase were increased slightly less during the whole fermentation process; the order of maximum endoglucanase, exoglucanase, β-glucosidase activities were fungal pretreatment > alkaline pretreatment > acid pretreatment > untreated.
These associations are determined by a pipeline process of applying the maximum-weighted- maximum-matching algorithm to the network model in Section Maximum Weighted Matching Inference model, calculating cumulative weights per disease in Section Prioritization of disease candidates, and applying the disease ranking scheme in Section Disease ranking scheme.
