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Consequently, the second law analyses of these systems using the LTNE method have been a point of focus in a number of more recent investigations.
The thorough and detailed analyses of these systems have provided deep insights into structure and function of prokaryotic antisense RNAs and the kinetics of antisense/sense RNA interaction.
In this work, the recent advances in the second law analyses of these systems are reviewed with an emphasis on the theoretical and modeling aspects.
The further analyses of these systems and their genomic and metabolic characteristics will continue to provide clues into the adaptations these endosymbiotic bacteria have suffered to accommodate to their hosts' peculiar diets.
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Analyses of these learning systems throw new light on the effectiveness of the different multimedia combinations to improve web-based learning of procedural tasks.
Molecular-level analyses of these complex systems critically depend on the integrated use of high-performance separation, high-resolution organic structural spectroscopy and mathematical data treatment.
Thermal and structural analyses of the system supported the design.
This necessitates probabilistic analyses of the system performance, which include probabilistic power flow (PPF).
Network biology provides comprehensive analyses of the system's components (nodes) and the relationships among them (edges).
This stochastic automaton allows multi-agent type of simulations that admit dynamical (temporal and spatial) analyses of the system and also tolerates very small concentrations of molecules.
52, 66, 67 NBO population analyses on some of these systems have been carried out in the past, and it has been noted that there is an almost linear relationship between the magnitude of the interaction energy and the fraction of an electronic charge transferred on halogen bond formation (negative charge is transferred from ammonia to the dihalogen).
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