Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Higgs boson. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Prediction based mean-variance model for constrained portfolio assets selection using multiobjective evolutionary algorithms

Abstract: In this paper, a novel prediction based mean-variance (PBMV) model has been proposed, as an alternative to the conventional Markowitz mean-variance model, to solve the constrained portfolio optimization problem. In the Markowitz mean-variance model, the expected future return is taken as the mean of the past returns, which is incorrect. In the proposed model, first the expected future returns are predicted, using a low complexity heuristic functional link artificial neural network (HFLANN) model and the portfolio optimization task is carried out by using multi-objective evolutionary algorithms (MOEAs). In this paper, swarm intelligence based, multiobjective optimization algorithm, namely self-regulating multiobjective particle swarm optimization (SR-MOPSO) has also been proposed and employed efficiently to solve this important problem. The Pareto solutions obtained by applying two other competitive MOEAs and using the proposed PBMV models and Markowitz mean-variance model have been compared, considering six performance metrics and the Pareto fronts. Moreover, in the present study, the nonparametric statistical analysis using the Sign test and Wilcoxon rank test are also carried out, to compare the performance of the algorithms pair wise. It is observed that, the proposed PBMV model based approach provides better Pareto solutions, maintaining adequate diversity, and also quite comparable to the Markowitz model. From the simulation result, it is observed that the self regulating multiobjective particle swarm optimization (SR-MOPSO) algorithm based on PBMV model, provides the best Pareto solutions amongst those offered by other MOEAs.

Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at √s = 13 TeV

Abstract: A search for dark matter (DM) particles is performed using events with a Higgs boson candidate and large missing transverse momentum. The analysis is based on proton- proton collision data at a center-of-mass energy of 13 TeV collected by the CMS experiment at the LHC in 2016, corresponding to an integrated luminosity of 35.9 fb−1. The search is performed in five Higgs boson decay channels: $$ \mathrm{h}\to \mathrm{b}\overline{\mathrm{b}} $$ , γγ, τ+τ−, W+W−, and ZZ. The results from the individual channels are combined to maximize the sensitivity of the analysis. No significant excess over the expected standard model background is observed in any of the five channels or in their combination. Limits are set on DM production in the context of two simplified models. The results are also interpreted in terms of a spin-independent DM-nucleon scattering cross section and compared to those from direct-detection DM experiments. This is the first search for DM particles produced in association with a Higgs boson decaying to a pair of W or Z bosons, and the first statistical combination based on five Higgs boson decay channels.

Ruppeiner geometry, phase transitions and microstructures of black holes in massive gravity

Abstract: Using the new normalized thermodynamic scalar curvature, we investigate the microstructures and phase transitions of black holes in massive gravity for horizons of various topologies. We find that ...

A comparative study of macrophytes influence on performance of hybrid vermifilter for dairy wastewater treatment

Abstract: Population increase and speedy urbanization are two major driving factors for scarcity of water in present time. To fulfil such water demand, reuse of industrial wastewater is necessary after essential purification. Vermifilter coupled with macrophyte filter is one of the promising technology for the treatment of organics and nutrient rich wastewater. In the present study, various macrophytes have been planted in vermifilter to study their effect on pollutants removal efficiency. Four sets of reactors R1, R2, R3 and R4 were designed and each set of reactor consisted of two units: vertical flow (VF) and horizontal flow (HF) unit. R1, R2 and R3 were planted with Canna indica, Saccharum spontaneum and Typha angustifolia, respectively, whereas R4 was without any macrophyte. Earthworm Eisenia fetida was inserted in all the four reactors. VF units of all hybrid vermifilter were loaded with synthetic dairy wastewater at hydraulic loading rate (HLR) of 0.6 m d−1. Effluents were collected at the bottom of the VF unit and channelled to the HF unit. Final effluent was collected at the end of the HF unit. The order of average percentage removal of COD was R1 (82.8%) > R3 (78.9%) > R2 (75.5%) > R4 (69.4%). Organics and nutrients were targeted to remove in the filter as these pollutants make major fraction in real dairy wastewater compare to antibiotics and other emerging contaminants. Presence of very few kind and low amount of antibiotics in dairy wastewater like lincomycin, chlortetracycline, etc. have been reported in literature.