Comparing with stationary approaches which generate only one expected total cost curve and therefore only one design flood estimation, the proposed new approach generate design flood estimation intervals and the 'Risk-Cost' approach selects a design flood value from the intervals based on the trade-offs between extreme flood risk and expected total cost.
In contrast to these approaches which generate mutations in rapidly growing cells, recent research showed that mutations could be generated in non-dividing cells under stressful but non-lethal conditions, leading to the birth of the theory of stress-induced mutagenesis (SIM).
Then, all the data were analyzed by using factor analysis approach which generated from Statistical Package for Social Science (SPSS) software.
Gaines and Shaw (1995) took a different approach which generated one node at a time following the learners' clicking activity on the web.
In this report, we immunoprecipitated small RNAs bound to EhAGO2-2 and sequenced them using a high throughput pyrosequencing approach, which generated over 360,000 small RNA reads.
The low-coverage 1000 Genomes approach, which generated whole genome sequencing data of 179 individuals with different ancestry (YRI, CEU and EAS), is estimated to have relatively low power to detect singleton SNPs or rare variants [ 23].
The genotyping-by-sequencing approach which generated these data used restriction enzymes to cut up the genome into DNA fragments that are distributed across the genome, sequenced tens of millions of these fragments on the Illumina next-generation sequencing platform, and aligned the fragments to discover genetic variation (facilitated by each specimen being individually barcoded).
The results show clear advantages of using the SLRR approach which generates films with nearly two times lower roughness than the ones grown by bulk deposition and SMG.
Thus, this paper describes a behavioral modeling approach which generates accurate empirical models for RF/microwave devices and that can be easily integrated into a VHDL-AMS simulator.
In addition, we show that a naïve approach, which generates adaptive motion by using machine learning methods by the interpolation between feasible training motion examples fails to guarantee the stability and dynamic feasibility of the generated behaviors.
