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The purpose of this paper is to perform the computational fluid dynamics (CFD) simulation with experimental validation to investigate the particle segregation effect in abrupt and smooth shapes circulating fluidized bed (CFB) risers.

The experimental investigations were carried out in lab-scale CFB systems and the CFD simulations were performed by using commercial software BARRACUDA. Special attention was paid to investigate the gas-particle flow behavior at the top of the riser with three different superficial velocities, namely, 4, 6 and 7.7 m/s. Here, a CFD-based noble simulation approach called multi-phase particle in cell (MP-PIC) was used to investigate the effect of traditional drag models (Wen-Yu, Ergun, Wen-Yu-Ergun and Richardson-Davidson-Harrison) on particle flow characteristics in CFB riser.

Findings from the experimentations revealed that the increase in gas velocity leads to decrease the mixing index inside the riser. Moreover, the solid holdup found more in abrupt riser than smooth riser at the constant gas velocity. Despite the more experimental investigations, the findings with CFD simulations revealed that the MP-PIC approach, which was combined with different drag models could be more effective for the practical (industrial) design of CFB riser. Well agreement was found between the simulation and experimental outputs. The simulation work was compared with experimental data, which shows the good agreement (<4%).

The experimental and simulation study performed in this research study constitutes an easy-to-use with different drag coefficient. The proposed MP-PIC model is more effective for large particles fluidized bed, which can be helpful for further research on industrial gas-particle fluidized bed reactors. This study is expected to give throughout the analysis of CFB hydrodynamics with further exploration of overall fluidization.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

Ideas regarding the basic character of humanity assume importance wherever people interact with one another — from the family to the political state, to the business enterprise. These conceptions, ranging from pessimism to optimism, from notions that evil, predatory competition on the one hand to goodness, co‐ operation and virtue on the other characterise the intrinsic disposition of people, derive from the culture to which the individual belongs, moulding his values and conditioning his behaviour. They not only affect the quality of human relations present in any collectivity, but exercise critical influence on the theories and practices of social control. The understanding of a range of social parameters is considerably enhanced when viewed from the perspective of prevailing cultural ideas about human nature itself.

To assess the state of supply chain management (SCM) research in India and to understand the research trends and methodologies used. The present study also aims to create a taxonomy of the subject areas researched in India.

The present study employed the systematic literature review methodology. Literature from 395 peer journal papers in 67 leading journals over a 20-year period (2000–2020 Quarter-1) was comprehensively reviewed and assessed.

SCM research in India started around the year 2000. The quantum of research was low (single digit) until 2010. There has been steady growth over the last decade, and over 50% of the total papers up until now has been published in the last four years. The present study created a three-tiered taxonomy of the subject areas and classified the papers as per it. The first tier (level-1) has seven categories (SCM strategy, network design, SCM processes and integration, IT systems, skills, performance measurement and others). A perusal of the newly created taxonomy revealed that, except for a few areas under level-1 categories (such as SCM processes and SCM strategy), the other level-1 categories have not seen much research. Similarly, there is little or no research in a large number of level-2 categories (such as outsourcing strategy, channel strategy, demand management, demand fulfillment, customer relationship management, integrated supply chain planning, new product development, returns, supply chain orientation, performance monitoring, performance improvement, SCM adoption process, SCM implementation issues and quantified benefits of SCM). Methodologically, the rigor of SCM research in India needs improvement.

A comprehensive taxonomy of SCM subject areas researched in India at three cascading levels was created for the first time in the present study. The taxonomy will help provide researchers with a clear understanding of the structure of the subject areas and help in identifying areas where research has been carried out and the subject areas where gaps exist for future research to proceed. The present study also provides an overview of the methodological rigor of SCM research in India and points out some of the limitations that researchers should avoid in future studies.

The conflicting results on the corrosion characteristics of aluminium matrix composites reinforced with agrarian waste have stimulated an investigation on the hardness and corrosion rate of sugar palm fibre ash (SPFA) reinforced LM26 Al/alloy composite by varying the SPFA from 0 to 10 wt% in an interval of 2 wt%. This paper aims to discuss the aforementioned issue.

The composites were produced via stir-casting and the hardness was determined using a Vickers hardness testing machine, corrosion rate was examined through the weight loss method by immersion in 0.5, 1.0 and 1.5 M hydrochloric acid (HCl) at temperatures of 303, 318, and 333 K for the maximum duration of 120 h. The morphological study was conducted using a scanning electron microscope (SEM) on the samples before and after immersion in HCl.

The incorporation of SPFA improved the hardness of the alloy from 58.22 to 93.62 VH after 10 wt% addition. The corrosion rate increases with increased content of SPFA, the concentration of HCl and temperature. The least corrosion rate of 0.0272 mpy was observed for the LM26 Al alloy in 0.5 M after 24 h while the highest corrosion rate of 0.8511 mpy was recorded for LM26 Al/10 wt% SPFA in 1.5 M HCl acid after 120 h. The SEM image of corroded samples revealed an increased number of pits with increased SPFA content.

The work is limited to SPFA up to 10 wt% as reinforcement in LM26 Al alloy, the use of HCl as corrosion medium, temperatures in the range of 303–333 K, and a weight loss method were used to evaluate the corrosion rate.

The corrosion rate was determined for LM26 Al/SPFA composites with various amounts of SPFA in 0.5, 1.0 and 1.5 M HCl at 303, 318 and 333 K and compared with the matrix alloy.

Industry 4.0 is regarded as a modern industrial phase wherein convergence between horizontal and vertical production systems, along with their interactions, would enable industries to obtain excellent industrial efficiency. This research thus explores the barriers relevant to Industry 4.0 and their cause and effect relations using the decision-making trial and evaluation laboratory (DEMATEL) technique, and this will assist in the implementation of Industry 4.0 in manufacturing industries.

A comprehensive literature review is carried out to outline the barriers for the Industry 4.0 implementation. The investigative techniques used in this research were conducted utilizing a questionnaire as an instrument for collecting data. The data collected were analysed using the method DEMATEL.

The findings indicate that key barriers to implementing Industry 4.0 include their cause and effect relationships. Reported “implementation costs” have been a critical barrier for Industry 4.0 implementation. Prioritization of barriers and their cause and effect relationships was achieved so that decision-makers could concentrate on overcoming such barriers for Industry 4.0 implementation.

The research is intended to assist executives in recognizing the barriers to Industry 4.0 implementation in their manufacturing industries. Furthermore, the strong functional and empirical importance of emerging and digital manufacturing technologies ensures that understanding the necessary barriers to implementation is important.

This is one of the first few empirical studies in the field of Indian manufacturing industries that uses the DEMATEL method framework as a foundation for recognizing cause-effect interactions among the barriers that affect Industry 4.0.

The purpose of this study is to discuss the construction of a structural measurement model utilizing structural equation modelling (SEM) to confirm the link between Industry 4.0 technologies, sustainable manufacturing practices and organizational sustainable performance. Relationship among the paradigm has yet to be fully investigated, necessitating a more conceptual and empirical examination on what impact they have on organizational sustainable performance when used together.

Industry 4.0 and sustainable production practices aim to progress a company's business competitiveness, forming sustainable development that benefits manufacturing companies. The aim of the study is to analyze the relationship between constructs that lead to operational excellence in firms that use Industry 4.0 technologies and sustainable manufacturing techniques. Experts from diverse automotive industries, who are applying both Industry 4.0 and sustainable manufacturing practices, provided data for the study.

Statistical estimations (hypotheses) are created to substantiate the measurement model that has been developed. The structural model was analysed, and the findings were discussed. The statistical estimate is either approved or rejected based on the findings. According to the conclusions of this study, strong link exists between Industry 4.0 technologies and sustainable manufacturing practices that affect organizational sustainable performance environmentally, economically and socially.

The research was conducted in the framework of automobile component manufacturing companies in India. The outcomes of the study are practically feasible.

The authors' novel contribution is the construction of a structural model with Industry 4.0 technologies and sustainable manufacturing practices into account.