Compared to the quenched sample, the Seebeck coefficient increases about 10% and the lattice thermal conductivity of the optimization sample is reduced about 30% at 700 K.
Version 1.0 of M3O includes Deterministic and Stochastic Dynamic Programming, Implicit Stochastic Optimization, Sampling Stochastic Dynamic Programming, fitted Q-iteration, Evolutionary Multi-Objective Direct Policy Search, and Model Predictive Control.
After optimization, all samples were analyzed with the optimized extraction conditions that allowed for identify 33 VOCs, belonging to different chemical groups namely hydrocarbon monoterpenes, hydrocarbon sesquiterpenes, oxygenated monoterpenes, oxygenated sesquiterpenes and other compounds.
For the method optimization, the samples were collected from the same area but from the field where the target pesticides had not been sprayed.
During the optimization, all samples were tested in triplicate.
Following optimization, two samples from separate experiments were sent for protein sequencing by mass spectrometry (MS).
Experimental designs for seven factors (HNO3, H2SO4 and H2O2 volumes, digestion time, pre-digestion time, temperature of the hot plate and sample weight) were used for optimization of sample digestion procedures.
We used the JoinMap default spatial sampling thresholds (0.10, 0.05, 0.03, 0.02, and 0.01) and three rounds of optimization per sample.
The average recognition accuracy of unit saccadic signals achieved 99.0% (before sample optimization) and 99.57% (sample optimized) over 10 participants, which reveals that the proposed algorithm presents an excellent classification performance in saccadic signals recognition.
