DNA Binding and Photocleavage Studies of Cobalt(III) Ethylenediamine Pyridine Complexes: [Co(en)2(py)2]3+ and [Co(en)2(mepy)2]3+

Two novel cobalt(III) pyridine complexes (1) [Co(en)2(py)2]3+ and (2) [Co(en)2(mepy)2]3+ (en=ethylenediamine, py=pyridine, and mepy=methylpyridine) have been synthesized and characterized. The interaction of these complexes with calf thymus DNA was investigated by absorption, emission spectroscopy, viscosity measurements, DNA melting, and DNA photocleavage. Results suggest that the two complexes bind to DNA via groove mode and complex 2 binds more strongly to CT DNA than complex 1. Moreover, these Co(III) complexes have been found to promote the photocleavage of plasmid DNA pBR322 under irradiation at 365 nm, cytotoxicity results of complexes are also showing anticancer activity

LATTICE BOLTZMANN SIMULATION OF STEADY AND OSCILLATORY FLOWS IN LID-DRIVEN CUBIC CAVITY

Flow in a three-dimensional lid-driven cavity (LDC) is studied using the lattice Boltzmann method (LBM) for various values of Reynolds number (Re). The 1000 Re flow is simulated on two grid sizes: viz. 225(3) and 301(3). We further compute the Richardson extrapolated flow profiles. Our results agree quantitatively with the spectral simulation results. We observe a steady to oscillatory transition of the flow in the range of Re [1900, 2000]

Controlling Autoreclosing on Overhead Lines with Underground Cable Sections Using Traveling-Wave Fault Location Based on Measurements from Line Terminals Only

The paper explains principles of fault locating based on traveling waves measured only at line terminals for hybrid lines comprising overhead and cable sections. The paper introduces an adaptive autoreclosing control logic to allow or cancel reclosing based on the location of the fault. The paper includes examples that explain and illustrate these principles

Machine learning based knowledge discovery and modeling of silicon content of molten iron from a blast furnace

It is necessary to maintain the silicon content of hot metal in a stipulated range to improve the productivity and energy consumption of a blast furnace. Significant research therefore went into the development of data-driven models to predict hot metal silicon content in real-time. However, these models use only a small subset of blast furnace variables that are chosen using prior process knowledge. As each blast furnace is unique in its operation, using pre-selected variables would lead to sub-optimal models. To address this, a machine learning based ensemble feature selection and modeling approach is proposed. In this approach, all the available furnace variables are ranked using multiple feature selection techniques based on their impact on silicon content. The individual ranks are combined to obtain an ensemble ranking of variables and the top variables in the ranking are used to build data-driven silicon prediction models. This approach is applied to an industrial blast furnace wherein 374 variables are used to obtain the ensemble ranking. While some of the top 100 variables in the ensemble ranking matched those that are commonly used in silicon predictions models, several new variables have also been identified. Silicon prediction models trained using the top 100 variables resulted in a hot rate of ~90% demonstrating the efficacy of the proposed approach. Real-time predictions from the models will enable blast furnace operators to control the silicon content without having to wait for laboratory results