The behaviour of consumers mostly follows the guidelines derived from marketing theories and models. But under some unavoidable circumstances, the consumers show a complete deviation compared to their existing consumption pattern, purchase behaviour, decision-making and so on. Under similar circumstances, this study aims to capture both urban and rural Bottom of the Pyramid (BoP) consumers’ perceptions of various marketing mixes during the COVID-19 pandemic situation. With a sample size of 378 and 282, the perception towards different marketing mixes has been captured for Pre-COVID and During-COVID periods, respectively. The adopted quantitative analysis indicates a difference in perception towards marketing mix During COVID compared to Pre-COVID. Moreover, the selection of West Bengal, India, as an area of research fulfills the BoP literature’s existing prominent research gap. This study also comes with the potential to assist marketers and the Fast-Moving Consumer Goods (FMCG) industry in framing strategies to target BoP consumers.
Electrical energy plays an important role in the day-to-day life and backbone for the industries. Today life without electricity cannot be imagined. Because of the unnecessary actions taken by human beings, wastage and theft of power are increasing day by day. Theft of electricity is the criminal practice of stealing electrical power. It is a crime and is punishable by fines and/or incarceration. It belongs to the non-technical losses. The objective of this work is to design a system that will try to minimize the illegal use of electricity and also reduce the chances of theft, and if theft happens appropriate actions will be taken. To identify and control power theft here, an intelligent system is introduced. It consists of two current transformers that are used to measure the actual load current and the other is to measure the turning back or neutral current. Those two current signals are fed to the micro controller. The micro controller will compare these two current signals. Depending on the comparison made by the micro controller it concludes whether power is theft through bypassing the energy.
Mixture models are well-known for their versatility, and the Bayesian paradigm is a suitable platform for mixture analysis, particularly when the number of components is unknown. Bhattacharya ( 2008) introduced a mixture model based on the Dirichlet process, where an upper bound on the unknown number of components is to be specified. Here we consider a Bayesian asymptotic framework for objectively specifying the upper bound, which we assume to depend on the sample size. In particular, we define a Bayesian analogue of the mean integrated squared error (Bayesian M ISE), and select that form of the upper bound, and also that form of the precision parameter of the underlying Dirichlet process, for which Bayesian M ISE of a specific density estimator, which is a suitable modification of the Polya-urn based prior predictive model, converges at sufficiently fast rate. As a byproduct of our approach, we investigate asymptotic choice of the precision parameter of the traditional Dirichlet process mixture model; the density estimator we consider here is a modification of the prior predictive distribution of Escobar & West (1995) associated with the Polya urn model. Various asymptotic issues related to the two aforementioned mixtures, including comparative performances, are also investigated. We also perform simulation experiments for comparing the performances of the approaches associated with Bhattacharya (2008) and Escobar & West (1995) in terms of Bayesian M ISE for various choices of the true, data-generating distribution, and demonstrate that the approaches related to Bhattacharya (2008) generally outperform those associated with Escobar & West (1995).
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