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...     

Ionic liquid-based deep eutectic solvents as novel solvent-cum-catalyst media for thermal dehydrogenation of chemical hydrides

The current work explores the usage of novel synthesized Deep Eutectic Solvent (DES) as a catalyst cum solvent media for the thermal dehydrogenation of chemical hydrides, namely Ammonia Borane (AB) and Ethylene diamine bisborane (EDAB). In the first instance, the quantum chemistry based COSMO-SAC (COnductor like Screening MOdel Segment Activity Coefficient) model was used for the selection of the pertinent solvent. 1-Butyl-3-methylimidazolium methanesulfonate: Imidazole ([BMIM][MeSO₃]:[Im]) turned out to be an ideal eutectic mixture with the highest predicted solubility with amine boranes. The DES was synthesized by combining the Hydrogen Bond Acceptor (HBA), namely 1-Butyl-3-methylimidazolium methanesulfonate and Imidazole as Hydrogen Bond Donor (HBD) at a molar ratio of 1:2 and T = 70 °C. The formation of DES was confirmed by recording the NMR spectra. Further, the thermal dehydrogenation study was performed at a vacuum of 4 × 10^?2 mbar (gauge pressure) of AB/DES and EDAB/DES systems at 105 °C, where a hydrogen equivalent of 1.40 and 2.55 was produced, respectively. The residual samples were further analyzed through ¹H NMR analysis for the reaction mechanism and to confirm the role of Ionic Liquid-based DES as catalyst cum solvent media.     

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.     

Spray dryer processed graphene oxide/reduced graphene oxide for high-performance supercapacitor

This study deals with the utility of mini spray dryer process to improve the dispersibility, of graphene oxide(GO) and its application for high-performance supercapacitor. Initially, the neutral solution of GO was obtained using the modified Hummer's method. After this, the prepared GO solution was processed by mini spray dryer to obtain a more purified, lighter, and dispersed form of GO which is named as spray dryer processed GO (SPGO). The SPGO thus obtained showed excellent dispersibility behavior with various solvents, which is not found in case of conventional oven drying. Furthermore, utility of SPGO and its reduced form (r-SPGO) for supercapacitor applications have been investigated. Results obtained from the cyclic voltammetry(CV) analysis, impedance, and charge-discharge behavior of supercapacitor fabricated using r-SPGO shows enhanced features. Therefore, the simple spray dried GO and its reduced form, that is, r-SPGO can be utilized as a potential candidate for the supercapacitor application. Herein, as synthesized SPGO exhibited the specific capacitance of 12.07 and 37.6 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively, at a scan rate of 5 mV/s. On the other hand, reduced form of SPGO, that is, r-SPGO showed the specific capacitance of 27.16 and 230 F/g with PVA-H₃PO₄ and 1 mol/L H₃PO₄, respectively.     

Detecting DDoS Attacks in Cloud Computing Using Extreme Learning Machine and Adaptive Differential Evolution

Wireless Personal Communications - Distributed denial of service (DDoS) attacks disrupt the availability of cloud services. The detection of these attacks is a major challenge in the cloud...     

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.

     

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.