Bacterial elongation factor G (EF-G) physically associates with translocation-competent ribosomes and facilitates transition to the subsequent codon through the coordinate binding and hydrolysis of GTP. In order to investigate the amino acid positions necessary for EF-G functions, a series of mutations were constructed in the EF-G structural gene (fusA) of Escherichia coli, specifically at positions flanking the effector domain. A mutated allele was isolated in which the wild-type sequence from codons 29 to 47 ("EFG2947") was replaced with a sequence encoding 28 amino acids from ribosomal protein S7. This mutated gene was unable to complement a fusAts strain when supplied in trans at the nonpermissive temperature. In vitro biochemical analysis demonstrated that nucleotide crosslinking was unaffected in EFG2947, while ribosome binding appeared to be completely abolished. A series of point mutations created within this region, encoding L30A, Y32A, H37A, and K38A were shown to give rise to fully functional proteins, suggesting that side chains of these individual residues are not essential for EF-G function. A sixth mutant, E41A, was found to inefficiently rescue growth in a fusAts background, and was also unable to bind ribosomes normally in vitro. In contrast E41Q could restore growth at the nonpermissive temperature. These results can be explained within the context of a three-dimensional model for the effector region of EF-G. This model indicates that the effector domain contains a negative potential field that may be important for ribosome binding.
Application of electric fields demonstrates great promise as an active heat transfer enhancement technique. Here, we study experimentally some characteristics of isolated bubbles such as bubble departure diameter, nucleation rate (frequency), and density of nucleation sites under the influence of an electric field In the nucleate pool boiling regime, isolated bubbles were recorded with a high-speed video camera at a rate of 1000 frames per second.The quantitative and qualitative analyses of bubble characteristics, and latent heat transfer concluded that at a constant heat flux condition in "the isolated bubble regime", the "density of nucleation sites" was a very sensitive boiling parameter when electric field varied. The latent heat contribution varied proportional with the density of nucleation sites when heat flux and/or electrode voltage varied. This further simplifies the complex task of modeling of the EHD enhanced pool boiling heat transfer in the isolated bubble regime.
Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.