Trend and efficiency analysis of co-authorship network

Although co-authorship in scientific research has a long history the analysis of co-authorship network to explore scientific collaboration among authors is a relatively new research area. Studies of current literature about co-authorship networks mostly give emphasis to understand patterns of scientific collaborations, to capture collaborative statistics, and to propose valid and reliable measures for identifying prominent author(s). However, there is no such study in the literature which conducts a longitudinal analysis of co-authorship networks. Using a dataset that spans over 20 years, this paper attempts to explore efficiency and trend of co-authorship networks. Two scientists are considered connected if they have co-authored a paper, and these types of connections between two scientists eventually constitute co-authorship networks. Co-authorship networks evolve among researchers over time in specific research domains as well as in interdisciplinary research areas. Scientists from diverse research areas and different geographical locations may participate in one specific co-authorship network whereas an individual scientist may belong to different co-authorship networks. In this paper, we study a longitudinal co-authorship network of a specific scientific research area. By applying approaches to analyze longitudinal network data, in addition to known methods and measures of current co-authorship literature, we explore a co-authorship network of a relatively young and emerging research discipline to understand its trend of evolution pattern and proximity of efficiency.     

Multi-haul quasi network flow model for vertical alignment optimization

The vertical alignment optimization problem for road design aims to generate a vertical alignment of a new road with a minimum cost, while satisfying safety and design constraints. A new model called multi-haul quasi network flow (MH-QNF) for vertical alignment optimization is presented with the goal of improving the accuracy and reliability of previous mixed integer linear programming models. The performance of the new model is compared with two state-of-the-art models in the field: the complete transportation graph (CTG) and the quasi network flow (QNF) models. The numerical results show that, within a 1% relative error, the proposed model is robust and solves more than 93% of test problems compared to 82% for the CTG and none for the QNF. Moreover, the MH-QNF model solves the problems approximately eight times faster than the CTG model.     

Evaluation of Global Water Resources Reanalysis Data for Estimating Flood Events in the Brahmaputra River Basin

Water Resources Management - Bangladesh and India are in a long-standing conflict with regard to the sharing of hydro-meteorological information of the Brahamaputra River. Consequently, it limits...     

Effect of terbium doping in bismuth ferrite nanoparticles for the degradation of organic pollutant under sunlight irradiation

Journal of Materials Science: Materials in Electronics - Herein, we have studied the influence of terbium (Tb) doping in bismuth ferrite (BiFeO3/BFO) nanoparticles towards its photocatalytic...