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This paper aims to investigate the effect of delayed water curing on the mechanical and microstructural properties of fly ash-based geopolymer paste-blended with Ground Granulated Blast Furnace Slag (GGBS) with different rest periods.

The blended geopolymer paste was composed of GGBS (15 per cent of the total weight) and the base material, Fly Ash (FA). The blended mix was activated by activator solution (Sodium hydroxide and Sodium silicate) containing 6 per cent Na₂O of total base material. The effect of delayed water curing has been studied by gradually increasing the aging period (Rest Period) from 2 hours to 24 hours in the formation of activated outcome along with Calcium Silicate Hydrate (CSH). To analyze the mechanical and microstructural properties of the resultant blended geopolymer paste, compressive strength test, FESEM and XRD have been carried out. Moreover, a long-term durability test subjected to sulphate exposure has been performed to evaluate the durability of the designed sustainable geopolymer paste.

The present paper shows that the delayed water curing incorporates secondary heat input enhancing the partial polymer formation along with CSH. Slag-blended AAFA-based geopolymer paste is seen to exhibit quick setting property. Also, AAFA-based geopolymer paste samples subjected to longer rest period show early strength gain at a high rate under water curing as compared to those subjected to the shorter rest period.

To the best of authors’ knowledge, the effect of delayed water curing on the mechanical and microstructural properties of slag-blended AAFA-based geopolymer paste has not been studied before.

Through a survey of 200 employees working in five of the thirty establishments analysed in previous research about the microeconomic effects of reducing the working time (Cahier 25), the consequences on employees of such a reduction can be assessed; and relevant attitudes and aspirations better known.

The decision of livelihood based on the embankment characters is essentially multivariate. Making an effort to do the bivariate modelling may eliminate the useful socio-economic information in the interdependent and simultaneous adaptation choices (Dorfman, 1996). Hence, the more appropriate method is multiple-choice decisions to livelihood adoption based on the embankment category. The purpose of this study is to find out whether the inhabitants of Sundarban really consider embankment as their “lifeline”, what they think about its sustainability and what the outer world thinks about the embankment.

To analyse this study, the multinomial logit (MNL) model has been used. This model gives a platform to study the influence of the factors on livelihood choice decisions. In this MNL model, the livelihood decisions are categorized based on their primary livelihood status at the survey. Thus, the choice of livelihood among individuals is explained in terms of the livelihood and the household characteristics.

This result can possibly explain the fact that increasing population or man power and increasing annual income and protection from embankment failure may reduce the need to choose any other form of economy apart from the indigenous one, as the society is dominated by farmers who own very small plots of land and face consequences like crop failure every year because of natural calamities. A unit increase in annual income would result in a 0.53% decrease in the probability of choosing labourer as occupation and 0.57% decrease in the probability of choosing fishing/“meen” collection as occupation.

The district is vast enough, and it is difficult to study all the blocks. Initially, nine blocks were identified as affected blocks from various literature reviews. Those blocks are Sagar, Patharpratima, Kultali, Gosaba, Kakdwip, Canning I, Canning II, Namkhana and Basanti. Pilot surveys were done to all those nine blocks identified above. After such a long and rigorous procedure, blocks were verified from available secondary data. Villages from vulnerable and less vulnerable parts of the later mentioned blocks are picked up as purposive sample, and household surveys are done on the basis of random sampling.

If the year of schooling is enhanced, then the tertiary sector gets benefited, but the indigenous society of Sundarban cannot depend on such a sector as the scope for development is very limited. Consequently, policies aiming at promoting adaptation to challenged livelihood need to emphasize the crucial role of providing basic needs for better production techniques; and more investment in this sector will surely enable villagers to adapt cultivation following age-old tradition.

The study uses the MNL model to investigate the factors guiding household choices of different occupational adaptation methods, and cultivation is found to be the automatic choice for the inhabitants of Sundarban. Cultivation is impossible without embankment. Thus, the embankment in Sundarban is considered, as “lifeline” is established. So it can be said that livelihood in this region depends on the stability of embankment. This age-old structure is susceptible to vulnerability because of its unscientific construction and improper maintenance. The main objective of this study is to find out whether the inhabitants of Sundarban really consider embankment as their “lifeline”, what they think about its sustainability and what the outer world thinks about the embankment.

Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

This paper aims to propose a reference model based simple strategy for the design of proportional-integral-derivative (PID) controller using frequency response matching for high-order stable, integrating and unstable processes that may have time-delay and non-minimum phase zero.

The reference sensitivity model is designed fulfilling stability conditions of the control system responses such as set-point response, load-disturbance response and noise response along with transient response criteria. The analytical controller thus designed is approximated to a PID controller using a simple formula based on a model-matching technique at low frequency.

PID controllers are designed for examples with varied dynamics taken from the literature, and the performances of the designed control systems are compared with some methods prevalent in the literature to show the efficacy of the proposed work. Overall, the method gives satisfactory set-point, as well as load-disturbance responses and controller-outputs in all the cases considered.

The method is applicable to high-order processes of various monotonic or oscillating dynamics without requiring process reduction. The PID controller designed considering a reference model with suitable criteria ensuring stability and a modified model matching technique, which provides a stable control system for all these high-order processes.

With an emphasis on spatial health disparities, this study examines how COVID-19 has affected healthcare access and inequality in India. The study developed the Healthcare Access Index (HAI) and Healthcare Inequality Index (HII) to assess the pandemic’s effects on healthcare. The study addresses spatial health disparities in healthcare access and inequality, filling gaps in the literature. The final aim of the study is to offer policy suggestions to lessen healthcare inequities in India, particularly in the context of COVID-19.

The study incorporates secondary data from publicly accessible databases such as the National Family Health Survey, Niti-Ayog and Indian Census databases and employs a quantitative research design. The impact of the COVID-19 pandemic on healthcare access and healthcare inequality in India is examined using the HAI and the HII. The five dimensions of healthcare access – availability, accessibility, accommodation, cost and acceptability – were used in developing the HAI. The study uses a panel data analysis methodology to examine the HAI and HII scores for 19 states over the pre-COVID-19 (2015) and post-COVID-19 (2020) periods. In order to investigate the connection between healthcare access, healthcare inequality and the COVID-19 pandemic, the analysis employs statistical tests such as descriptive statistics, correlation analysis, factor analysis and visualization analysis.

According to the study, COVID-19 impacted healthcare access and inequality in India, with notable regional inequalities between states. The pandemic has increased healthcare disparities by widening the gap between states with high and low HII ratings. Healthcare access is closely tied to healthcare inequality, with lower levels of access being associated with more significant levels of inequality. The report advises governmental initiatives to lessen healthcare disparities in India, such as raising healthcare spending, strengthening healthcare services in underperforming states and enhancing healthcare infrastructure.

For Indian healthcare authorities and practitioners, the study has significant ramifications. In light of the COVID-19 pandemic, there has been a main focus on addressing geographic gaps in healthcare access and inequality. The report suggests upgrading transportation infrastructure, lowering out-of-pocket costs, increasing health insurance coverage and enhancing healthcare infrastructure and services in underperforming states. The HAI and the HII are tools that policymakers can use to identify states needing immediate attention and appropriately spend resources. These doable recommendations provide a framework for lowering healthcare disparities in India and enhancing healthcare outcomes for all communities.

The study’s originality resides in establishing the HAI and HII indices, using panel data analysis and assessing healthcare inequality regarding geographic disparities. Policy choices targeted at lowering healthcare disparities and enhancing healthcare outcomes for all people in India can be informed by the study’s practical consequences.

Transitioning to a low-carbon economy requires a positive response by society, including business organizations, towards the green concept and also requires the implementation of long-term green strategies. These requirements could impose various transition risks on the sustainable development of the firms; hence, the present study aims to examine the impact of climate transition risk on a firm’s financial performance and market value creation from the Indian perspective.

We have considered the firm-level environmental risk score (ERS) to evaluate the sensitivity of a firm’s profitability (measured by ROA & ROE) and market value (measured by Tobin’s Q) towards the climate transition risk. The present study used multiple regression analysis to examine the impact of climate transition risk on the firm’s financial performance and market value creation, as evidenced by Nifty 50 companies.

The empirical results suggested that corporate climate transition risks have been positively associated with the firm’s financial performance indicators but negatively impacted the firm’s market value creation in the case of select Indian-listed firms. Hence, our results indicate that with the increase of firm-level climate transition risk, the firm’s financial performance increases but negatively affects the firm’s market value creation. The robustness tests have also confirmed the same results and supported our analysis.

The present paper contributes to the existing literature on climate risks and firms’ performance by providing insights about firms’ sensitivity towards climate transition risk from the Indian perspective.

In the post-WTO era, the volume of international trade has grown in a good amount in India. At the same time, the continuous lowering and removing of the trade barriers of different forms create several impacts on poverty and inequality. In this chapter, we tried to capture the issues of inequality, specially the gender inequality, which has worsened a lot in the last two decades. In one side, trade openness enhanced the growth, but at the cost of increasing inequality. Theil index and Atkinson index both show an increasing trend of inequality. The Gender Inequality Index (GII) and the Inequality-adjusted Human Development or Gender Development Indices are also showing increasing inequality. The state-wise analysis of such inequality indices is varying a lot over the study period. This chapter throws some insight into these issues and concludes that in the post-WTO era income inequality has increased a lot with a very few exceptions. In some states (like Maharashtra, Gujarat, Karnataka, and Punjab) only export have increased the employment in the unorganized sectors. The study concludes that exports have generated additional employment and incomes in the economy, but these gains have not trickled down to the poor. The study is confined to Indian cases only and covers the time period 2000–2001 to 2018–2019.