Finding answers to relevant questions

Faculty at FLAME University enjoys enormous opportunities and avenues for cutting-edge research in their respective areas of specialization strengthening the vision, awareness and synergy of the inter-disciplinary approach of education.

Excellence achieved through this research, in turn, creates new learning and further enriches the diversity of knowledge by synthesizing and integrating insights from a range of disciplines. This unique academic cooperation between multiple disciplines does transcend the boundaries of disciplines, and results in an insightful and consequential research output. This process of creating synergy between excellence and diversity - a dynamic motion that advances academic learning through constant interconnectivity - is the basic driving force that refines education and research at the University.



This study employs a Pooled Mean Group (PMG) estimation method to examine the effect of floods on rural agricultural wages, controlling for key wage determinants for 15 major Indian states over the period 1983–2011. The PMG estimates suggest that damages due to floods have a positive impact on annual agricultural wages and agricultural wages in flood months in the long run but an adverse effect in the short run. In other words, our findings indicate that annual agricultural wages and agricultural wages in flood months increase by 0.164 percent and 0.149 percent, respectively, in the long run, but they decline by 0.025 percent and 0.026 percent, respectively, in the short run when damages due to floods increase by 10 percent. Moreover, we find that better employment opportunities in rural non-agricultural sectors significantly increase agricultural wages in the long run. Our empirical findings are robust to alternative flood measures in terms of area affected by floods. In sum, we conclude that floods have differential impacts on agricultural wages in the short and long run, after taking into account the key wage determinants.


Trauma victims experience intense negative emotions during and post-trauma. These negative emotions may last for a long time and disrupt the normal functioning of an individual. Exposure to a traumatic event often leads to a threatened self-identity. The role of compensatory consumption in alleviating negative emotions and protecting from threats to self-identity has been addressed in the extant literature. The present study aims to explore the compensatory consumption behavior of trauma victims that stems from negative emotions and threats to selfidentity. The study also aims to understand the emotional changes and outcomes of this consumption as experienced by traumatized subjects. In-depth interviews were used as the primary data-collection method, aimed at eliciting thick descriptions from the respondents. The findings are discussed with regard to their practical and theoretical implications, as well as potential avenues for future research.



The study addresses the crucial issue of sustainable development goals (SDGs) and institutional voids in the peri-urban geographies of India. The peri-urban geographies, though within a cosmopolitical city, lack basic amenities like drinking water, sanitation and waste management. We study social entrepreneurial strategies to address these issues and thereby illustrate strategies that could be used to address sustainable development goals



Linear regression models are traditionally used to capture the relation between the input and output variables. Linear models cannot account for the nonlinear relations in the data. Hence, the prediction models may not be accurate. For this reason, machine learning-based models are being increasingly used. For modeling, design, and scale-up of rotating disc contactors (RDCs), rational estimation of dispersed-phase holdup and drop size is crucial. We have employed random forest (RF) and autoencoder–RF-based models for the prediction of dispersed-phase holdup and drop size in RDCs. Our results show that both these models predict drop size quite well. For holdup, the autoencoder–RF combination predictions are not satisfactory. The standalone RF model predictions generalize very well. RF-based models can be further used for prediction of different variables of interest in RDCs.



This paper analyses the initial and intense rounds of the 2020 Australian Open Men’s Singles matches on 14 match statistics. The findings show that the statistics which are important to winning in the initial rounds are not the same as those for winning in the intense rounds. In the initial rounds, the match winner performed better than the loser on receiving points won, second serve to win, first serve to win, breakpoints won, net points won, winners, total points won, unforced errors, aces, double faults, fastest serve speed, and average first-serve speed. However, to win the intense rounds, the winner performed better than the loser on first serve to win, receiving points won and net points won. The findings help the player and the coach to develop skills and techniques to devise a player strategy during the initial rounds and the intense rounds to win the tournament