This study has a novel approach to capture the attitude of Bottom of the Pyramid (BoP) consumers towards Packaging Influenced Purchase (PIP) during the Covid-19 crisis. Over the years, BoPs consumers have established themselves as an emerging market with ample growth and opportunities. The authors suggested a Multiple-Criteria Decision-Making (MCDM) based framework to assist marketers in targeting both urban and rural BoP consumers regarding PIP. Packaging elements and influence of family, extended family, peers have been included in the framework for gaining in-depth understanding. With a sample size of 100 from West Bengal, this focus group-based study can fulfil the BoP literature’s existing prominent research gap. Results indicate the difference in attitude for urban and rural BoPs towards PIP during this crisis. The fusion of MCDM based approach and relevant machine learning-based technique aims to assist marketers in identifying, formulating, and redefining an action plan.

Fault tolerance is one of the most important issues in cloud computing to provide reliable services. It is difficult to implement due to dynamic service infrastructures, complex configurations and different dependencies. Extensive research efforts have been made to implement fault tolerance in the cloud environment. Many studies focus only on fault detection and do not consider fault tolerance. For this reason, in this paper, in addition to recognizing the nature of the fault, a fuzzy logic-based approach is proposed to provide an appropriate response and increase the fault tolerance in the cloud environment. Checkpoint-based migration technique is used to increase fault tolerance. Using a checkpoint during migration can reduce time and processing costs and balance the load between virtual machines in the event of a fault. The simulation is performed according to the data center of Vietnam Telecommunications Company (VDC). The results of the proposed method in a period of 60 minutes show 98.03% fault detection accuracy, which is 4.5% and 4.1% superior to FLPT and PLBFT algorithms, respectively.

Low frequency oscillations result due to heavy loading conditions line faults, sudden change of generator output and also due to poor damping of interconnected power systems. There are different types of disturbances in the power system like sudden change of load, generation, faults, switching of lines. This hampers the power transmission capacity of the lines and the stability of the system There are significant impacts on the system stability during the charging and discharging operation of Electric Vehicle (EV). In the present work the charging operation of EV is considered as a load disturbance. The introduction of these vehicles in the system creates the problem of low frequency oscillation and endanger the system stability and security. In the present work the Single machine infinite bus system (SMIB) is first developed using mathematical modelling with consideration of EV disturbance. The LQR approach from optimal control theory is then applied in the system to damp the system oscillations, improving the system eigenvalues and enhancing the stability. The stability is seen in the system after LQR from various figures. In the second work the plotting of variation of different state variables is done using three different methods which are the transfer function model method, using code and then using state space representation of the system. The work is further extended by adding Power system stabilizer (PSS) to the system, again considering the EV disturbance. The time domain simulation results showed the improvement in stability using PSS device. Thus, in the present work the oscillations problems created due to the introduction of electric vehicles are solved by two methods. The first is implementing LQR approach from optimal control theory in the system and the second method is by adding PSS device in the same system.

At present, the whole world is experiencing a huge disturbance in social, economic, and political levels which may mostly attributed to sudden outbreak of Covid-19. The World Health Organization (WHO) declared it as Public Health crisis and global pandemic. Researchers across the globe have already proposed different outbreak models to impose various control measures fight against the novel corona virus. In order to overcome various challenges for the prediction of Covid-19 outbreaks, different mathematical and statistical approaches have been recommended by the researchers. The approaches used machine learning and deep learning based techniques which are capable of prediction of hidden patterns from large and complex datasets. The purpose of the present paper is to study different machine learning and deep learning based techniques used to identify and predict the pattern and performs some comparative analysis on the techniques. This paper contains a detailed summary of 40 paper based on this issue along with the use of method they applied to obtain the purpose. After the review it has been found that no model is fully capable of predicting it with accuracy. So, a hybrid model with better training should be employed for better result. This paper also studies different performance measures that researchers have used to show the efficiency of their proposed model.

Among the most important activities within a company we find that of quality management. This activity represents reflects the most rigorous way possible for a better organization of establishments in order to offer the best service to customers and to the various members of these establishments. This activity of quality management is a very delicate and sensitive task due to the large number of documents and business processes that are handled on a cyclical basis. For this reason, setting up a reliable and efficient system for managing the different aspects of the quality management process becomes a challenge for any company that seeks excellence. This article proposes a new intelligent approach to the need of the management of human and commercial resources within the companies for a good management of the process of quality management according to its own conception. Our approach allows any quality management manager to manage the different modules of a QMS according to the ISO 9001 standard through the different interfaces offered by our solution. The monitoring phase of this process through the implementation of a workflow orchestrator, jBpm.