Towards finding the best-fit distribution for OSN data

The Journal of Supercomputing - Currently, all online social networks (OSNs) are considered to follow a power-law distribution. In this paper, the degree distribution for multiple OSNs has been...     

A simulation study of reduction kinetics for sponge iron production in a rotary hearth furnace

ABSTRACT

A mathematical model has been developed by coupling genetic algorithm (GA) with heat and material balance equations to estimate rate parameters and solid-phase evolution related to the reduction of iron ore-coal composite pellets in a multi-layer bed Rotary hearth Furnace (RHF). The present process involves treating iron ore-coal composite pellets in a crucible over the hearth in RHF. The various solid phases evolved at the end of the process are estimated experimentally, and are used in conjunction with the model to estimate rate parameters. The predicted apparent activation energy for the wustite reduction step is found to be lower than those of the reduction of higher oxides. The thermal efficiency is found to decrease significantly with an increase in the carbon content of the pellet. Thermal efficiency was also found to increase mildly up to three layers. Multilayer bed remains as a potential design parameter to increase thermal efficiency.     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

     

Discovery of Affinity-Based Probes for Btk Occupancy Assays

Bruton's tyrosine kinase (Btk) is an attractive target for the treatment of a wide array of B-cell malignancies and autoimmune diseases. Small-molecule covalent irreversible Btk inhibitors targeting Cys481 have been developed for the treatment of such diseases. In clinical trials, probe molecules are required in occupancy studies to measure the level of engagement of the protein by these covalent irreversible inhibitors. The result of this pharmacodynamic (PD) activity provides guidance for appropriate dosage selection to optimize inhibition of the drug target and correlation of target inhibition with disease treatment efficacy. This information is crucial for successful evaluation of drug candidates in clinical trials. Based on the pyridine carboxamide scaffold of a novel solvent-accessible pocket (SAP) series of covalent irreversible Btk inhibitors, we successfully developed a potent and selective affinity-based biotinylated probe 12 (2-[(4-{4-[5-(1-{5-[(3aS,4S,6aR)-2-oxo-hexahydro-1H-thieno[3,4-d]imidazol-4-yl]pentanamido}-3,6,9,12-tetraoxapentadecan-15-amido)pentanoyl]piperazine-1-carbonyl}phenyl)amino]-6-[1-(prop-2-enoyl)piperidin-4-yl]pyridine-3-carboxamide). Compound 12 has been used in Btk occupancy assays for preclinical studies to determine the therapeutic efficacy of Btk inhibition in two mouse lupus models driven by TLR7 activation and type I interferon.     

An application of chemometric techniques to analyze the effects of the wave function modifications on the intermolecular stretching frequencies of the hydrogen-bonded complexes

Factorial design and principal component models are used to determine how ab initio H-bond stretching frequencies depend on characteristics of the molecular orbital wave functions of acetylene–HX, ethylene–HX and cyclopropane–HX π-type hydrogen complexes with X=F, Cl, CN, NC and CCH. The results obtained for the three sets of complexes show that factorial design and principal component analyses complement each other. Factorial design calculations clearly show that these frequencies are affected mostly by inclusion of electron correlation on the calculation level. On average, their values are increased by about 25 cm⁻¹ due to a change from the Hartree–Fock (HF) to Möller–Plesset 2 (MP2) level. Valence, diffuse and polarization main effects as well as valence–diffuse, diffuse–correlation and polarization–correlation interaction effects are also important to better describe a factorial model to the H-bond stretching frequencies of these hydrogen complexes. This simplified model has been successful in reproducing the complete ab initio results, which correspond to two hundred and forty calculations. Principal component analyses applied only to hydrogen-bonded complexes whose experimental frequencies are known, has revealed that the six-dimensional original space can be accurately represented by a bidimensional space defined by two principal components. Its graphical representation reveals that the experimental intermolecular stretching frequencies are in closest agreement with the MP2/6–31+G and MP2/6–311+G ab initio results.