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In order to assess the performance of an induction generator in standalone wind power application, it is imperative that mathematical models are developed that accurately represent the system and take into account various electromagnetic influences such as skin effect. The purpose of this paper is to utilize mathematical models to study the transient and steady‐state behaviour of the self‐excited induction generator (SEIG), in one case with an aluminum rotor, in another case, with a copper rotor, under various load conditions while taking the above factor into account.

Mathematical models of a SEIG in the d‐q axis frame have been developed based on the generalized machine theory. A relationship between the mutual inductance and the magnetizing current of the machine has been presented. The rotor impedances have been customized to include skin effect. Using these relations, the model has been extended to include the saturation and skin effects. In order to verify the accuracy of the models, numerical and experimental investigations have been carried out on two 7.5 hp aluminum‐rotor and copper‐rotor SEIGs.

It was found that the model that takes into account the saturation and skin effects produces numerical results that closely match experimental values for both the machines.

This paper describes how a model of an SEIG considering saturation and skin effect has been developed and applied to aluminum‐ and copper‐rotor machines of similar power ratings to analyze their performance.

This paper aims to present an improvement to the power quality of the grid by using a colliding body optimization (CBO) based proportional-integral (PI) compensated design for a grid-connected solar photovoltaic-fed brushless DC motor (BLDC)-driven water pumping system with a bidirectional power flow control. The system with bidirectional power flow allows driving the pump at full proportions uninterruptedly irrespective of the weather conditions and feeding a grid when water pumping is not required.

Here, power quality issue is taken care of by the optimal generation of the duty cycle of the voltage source converter. The duty cycle is optimally generated by optimal selection of the gains of the current controller (i.e. PI), with the CBO technique resulting in a nearly unity power factor as well as lower total harmonic distortion (THD) of input current. In the CBO technique, the gains of the PI controller are considered as agents and collide with each other to obtain the best value. The system is simulated using MATLAB/Simulink and validated in real time with OPAL RT simulator, OP5700.

It was found that the power quality of grid using the CBO technique has improved much better than the particle swarm optimization and Zeigler–Nichols approach. The bidirectional flow of control of VSC allowed for optimum resource utilization and full capacity of water pumping whatever may be weather conditions.

Improved power quality of grid by optimally generation of the duty cycle for the proposed system. A unit vector tamplate generation technique is used for bidirectional power transfer.

The purpose of this paper is to estimate aerodynamic parameters accurately from flight data in the presence of unknown noise characteristics.

The introduced adaptive filter scheme is composed of two parallel UKFs. At every time‐step, the master UKF estimates the states and parameters using the noise covariance obtained by the slave UKF, while the slave UKF estimates the noise covariance using the innovations generated by the master UKF. This real time innovation‐based adaptive unscented Kalman filter (UKF) is used to estimate aerodynamic parameters of aircraft in uncertain environment where noise characteristics are drastically changing.

The investigations are initially made on simulated flight data with moderate to high level of process noise and it is shown that all the aerodynamic parameter estimates are accurate. Results are analyzed based on Monte Carlo simulation with 4000 realizations. The efficacy of adaptive UKF in comparison with the other standard Kalman filters on the estimation of accurate flight stability and control derivatives from flight test data in the presence of noise, are also evaluated. It is found that adaptive UKF successfully attains better aerodynamic parameter estimation under the same condition of process noise intensity changes.

The presence of process noise complicates parameter estimation severely. Since the non‐measurable process noise makes the system stochastic, consequently, it requires a suitable state estimator to propagate the states for online estimation of aircraft aerodynamic parameters from flight data.

This is the first paper highlighting the process noise intensity change on real time estimation of flight stability and control parameters using adaptive unscented Kalman filter.

The use of the software package mini‐micro CDS/ISIS for the creation of the “Myco Database” is discussed. The design, development of the database and sorting of records are described step by step. Details of the print format and sample printout of the printed version of the directory are given.

In a pandemic framework (COVID-19), this chapter explores the impact of the global economy and socio-cultures concerning three axes: recreational, tourism, and hospitality. Although we slowly see an economic revival, it is well known that this sector of study is very susceptible to being affected by the context of nations. Following restrictions and measures taken by governments around the world to reduce the number of cases of coronavirus infections, many nations closed their borders, affecting international travel and by 2020 tourism had been reduced to the near cessation of operations due to the imminent fear of this poorly studied disease, and the service sector was negatively affected. It should be added that, according to the World Tourism Organization's projections, a decrease of between 20 and 30% is forecast for 2020 compared to the previous year.

The present study is aimed at understanding the survival strategies of Subsistence-type Rural Independent retailers, henceforth called SRIs, in the Bottom-of-the-Pyramid (BoP) markets of developing economies through a qualitative study. SRIs constitute a pivotal channel of distribution of goods to BoP consumers living in the rural areas of developing economies. A process of long interviews was chosen for data gathering to allow SRIs to go into details to allow them to expound upon their beliefs, life-situations, and societal norms. Narratives were collected verbatim from SRIs. The concept of socio-economic embeddedness is used as the central concept to interpret and connect the elements, discerned from the narratives, into a conceptual framework. The aforesaid theory combines the neo-classical economic concept of utility maximization with behavioral economics and economic sociology. The analysis of the narratives is interpretive against the identified elements of the concept of economic embeddedness. The survival strategies of SRIs seem to stem from sociological, psychological, and utility-maximizing behaviors. The elements of SRIs’ responses to its environment provide valuable insights into their purchase motivations.

India is a diverse country in which the development of services for children with disabilities presents many challenges. There is evidence that progress towards the provision of educational and therapeutic services has been considerable in the metropolitan cities, with reports of many examples of good and innovative practice. Rural communities are often less well served, and families living in some areas continue to face challenges when attempting to gain support for their children with disabilities. This chapter interrogates the situation for families in districts in southern and central India. The authors draw upon recent research to consider how the challenges of providing a multi-disciplinary support mechanism can be delivered to those living in these areas. Issues related to co-ordination of services, recruitment and retention of professionals and allocation of resources are considered through an examination of the socio-economic conditions confronted by families and professionals. Case study evidence from projects aimed at providing effective services are presented and discussed.

The persistent rise in the global discharges of carbon (CO₂) emissions and the likely undesirable consequences of this practice on the global atmosphere attracts the attention of policy makers and researchers to argue on the causes and perpetrators of CO₂ emissions at international level. The purpose of this paper is to examine the relationship between economic growth, energy production, capital formation, foreign direct investment (FDI) and CO₂ emissions in the LMI and Middle East and North African (MENA) countries for the period 1980–2011.

Two separate autoregressive distributed lag (ARDL) models were estimated for both the LMI and MENA countries, for the period 1980–2011. Furthermore, a fully modified OLS (FMOLS) was estimated for the two regions over the same period.

The results indicated that for the lower-middle income countries, there is a positive significant relationship between energy production and CO₂ emissions. In the long run while in the short run, FDI and EGP are positively related to CO₂ emissions while gross capital formation (GCF) has a negative impact on the CO₂ emissions in the short run over the same period. Similarly, for the MENA countries, there is a positive relationship between EGP, GCF and CO₂ emissions in both the short run and the long run. Furthermore, the estimated group mean FMOLS indicated that apart from GDP, all other variables have significant positive impact on CO₂ emissions.

The study covers only the period 1980–2011. This was because of limited available data during the study.

The study recommended the adoption of green technology by FDI firms and also in the process of energy production such as in crude oil production.

The study carried out a complex analysis where simultaneously all the countries of LMI and MENA regions where considered. Furthermore, separate analysis where conducted for each of the LMI and MENA regions using ARDL model. Variable representing energy production was included in the analysis which was not considered by previous studies. Lastly, FMOLS was estimated for the pooled of LMI and MENA countries which further distinguished the study from the relevant previous studies.

The aim of the paper is to investigate Goodwin’s growth cycle in the Indian organised manufacturing industries.

The methodology is based on bi-variate differential equation, econometrics model like log-linear regression and Autoregressive Distributed Lag model. An empirical investigation is conducted on data from the Annual Survey of Industries from 1980 to 2018 time period.

The results indicate that though the original Goodwin model estimates deviated from data estimates, its modified (neo-Goodwin) model are found to be equivalent to the data estimates. Moreover, in contrast to the original model, the capital accumulation rate (investment to profit ratio) is not assumed to be unitary in the modified Goodwin model. Furthermore, the labour market-led and cost effect conditions of the Goodwin cycle are empirically verified by investigating the interdependency between employment rate and wage share. Lastly, the short- and long-run Goodwin cycles are observed to be moving in anti-clockwise direction in the employment rate and wage share bi-dimensional plane, thus confirming the existence of profit-led distribution where wage share continuously reducing with high employment.

This study opens the discussion on application of capitalistic model in the emerging economy and also suggests to incorporate some theoretical models like Kaldorian, Keynesian, Kaleckian or Schumpetrian into the Goodwin cycle.

This is the first paper which empirically examines the capitalistic nature of Indian organised manufacturing industries through the lens of Goodwin growth cycle and then extend it to the Neo-Goodwin model by relaxing one of the unrealistic assumption regarding unitary investment to profit ratio.

This book chapter examines how the evolution of the tourist and hospitality industries has been impacted by digital technologies. Digitalisation makes it possible to implement resilient infrastructure in all applications to achieve sustainability. The hospitality and tourism sector is known to be information-rich due to its diverse commercial interactions with shareholders and constant evolution of managerial processes, modification of tourist and hotel services, advancements in technology and an intensely competitive atmosphere. It has been recognised that growth of tourism and hospitality industry becomes impossible without the applications of modern digital technologies that enable integration and communication, boost service quality and transmit a sizeable amount of information. In the context of the tourism sector, the establishment of resilient infrastructure that relies on digital technologies is vital in order to obtain optimal consumer feedback pertaining to the provision of high-quality service. The utilisation of digital technology has demonstrated its capacity to enhance hospitality services through the facilitation of real-time data-driven decision-making. The objective of this study is to emphasise the importance and practical uses of the internet of things (IoT), artificial intelligence (AI), cloud computing and big data in the context of consumer quality and satisfaction. This study also addresses the significance of various technologies and their applications in attaining consumer quality and pleasure in the digital realm. Additionally, it might help company owners, managers and marketers in any field to achieve and improve high business performance by executing the right plans to use AI that fulfil the demands and expectations of both customers and staff.