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The purpose of this paper is to discuss the flow of Casson nanofluid past a nonlinear permeable stretching sheet in the presence of thermal radiation, chemical reaction, viscous dissipation, heat source, and magnetohydrodynamics.

Appropriate transformations are used to convert the boundary layer equations into nonlinear ODEs which are then solved numerically by using the Runge-Kutta-Fehlberg fourth-fifth order method along with shooting technique.

Solution of this systems is obtained for velocity, temperature, and concentration profiles. Graphical illustrations are added to discuss the effect of evolving parameters against above-mentioned distributions. Tabular values of local skin friction factor, local Nusselt number, and local Sherwood number are also added and studied accordingly.

A good agreement of the present results has been observed by comparing with the existing literature results. It is noted that skin friction coefficient, Nusselt number, and Sherwood number decrease with Casson parameter and increase with suction parameter.

Layered manufacturing (LM) or rapid prototyping is a process in which a part is produced using layer‐by‐layer addition of the material. In LM, slicing of the CAD model of a part to be produced is one of the important steps. Slicing of CAD model with a very small slice thickness leads to large build time. At the same time if large slice thickness is chosen, the surface finish is very bad due to staircasing. These two contradicting issues namely reduction in build time and better surface quality have been a major concern in laminated manufacturing. This contradiction has led to the development of number of slicing procedures. The present paper reviews various slicing approaches developed for tessellated as well as actual CAD models.

This paper aims to provide an overview of the Organization Learning and Learning Organization concepts obtaining the perspectives of Professor Mary M. Crossan and presents an evolution of her immense contribution to the field over the past two decades.

A conversation with thought-leader, Professor Mary M. Crossan.

How different “character configurations” and “processes” enhance organization learning across levels in the organization.

The discussion with Professor Mary M. Crossan reveals her take on the evolution of the organizational learning framework and the significant role of the “Leader’s Character” in shaping organizational learning. Exploring this evolution provides the context and impetus to researchers and practice leaders to verify.

The provision of fair and remunerative prices to farmers through government intervention is one of the key debates to address the farmers' distress in India. This article identifies how different marketing channels are responsible for higher price realization over the officially announced minimum support price (MSP).

The study uses the NSSO-SAS, 2012–13 and NSSO-SAS, 2018–19 for Aggregate level data and Unit Level Data on the Situation Assessment Survey of Farmers' households. It uses logit regression to determine the factors responsible for better price realization.

Our major findings indicate that two factors importantly determine better price realization than MSP. Firstly, government agencies provide better prices for crops covered by MSP, such as paddy, wheat and cotton. However, the probability of receiving higher prices increases for some crops if the farmers belong to the upper land size classes and upper social category. Secondly, jowar, bajra, maize and ragi, other important crops that don't benefit from government agencies, may require higher levels of procurement at the state level.

The present study only analyzes selected major crops. Distance is an important factor in choosing a marketing channel that is not incorporated due to unavailability in NSS Data.

The study is based on the latest original empirical evidence and sheds light on the variation in price realization in different agricultural marketing channels in India.

This study presents the development of a supply chain (SC) observatory, which is a benchmarking solution to support companies within the same industry in understanding their positioning in terms of SC performance.

A case study is used to demonstrate the set-up of the observatory. Twelve experts on automatic equipment for the wrapping and packaging industry were asked to select a set of performance criteria taken from the literature and evaluate their importance for the chosen industry using multi-criteria decision-making (MCDM) techniques. To handle the high number of criteria without requiring a high amount of time-consuming effort from decision-makers (DMs), five subjective, parsimonious methods for criteria weighting are applied and compared.

A benchmarking methodology is presented and discussed, aimed at DMs in the considered industry. Ten companies were ranked with regard to SC performance. The ranking solution of the companies was on average robust since the general structure of the ranking was very similar for all five weighting methodologies, though simplified-analytic hierarchy process (AHP) was the method with the greatest ability to discriminate between the criteria of importance and was considered faster to carry out and more quickly understood by the decision-makers.

Developing an SC observatory usually requires managing a large number of alternatives and criteria. The developed methodology uses parsimonious weighting methods, providing DMs with an easy-to-use and time-saving tool. A future research step will be to complete the methodology by defining the minimum variation required for one or more criteria to reach a specific position in the ranking through the implementation of a post-fact analysis.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

This paper aims to investigate how the passage of the Sarbanes Oxley Act (SOX) impacted the likelihood and timing of the decision of leveraged buyout (LBO) firms to exit via initial public offering (IPO) (reverse-LBO) and the mediating effect of reputed private equity (PE) firms.

The sample comprises firms that went private via LBO between 1990 and 2018. The authors use logistic and ordinary least square regression models to compare the effect of SOX on the re-listing decision and the time taken to re-list.

LBO firms were less likely to exit via public offering after SOX, and the time from LBO to IPO was significantly longer for exiting firms post-SOX. PE firm reputation partially reversed the reluctance to exit via IPO and shortened the time to exit.

The primary focus is RLBOs; the authors do not directly examine other methods of LBO exit. The findings have policy implications for unintended impacts of SOX. Despite the benefits of increasing transparency and protecting investors, SOX reduced the likelihood of going public and increased the time to IPO, potentially reducing product market competition.

RLBOs present a unique experimental setting as the authors can test the impact of SOX on both the likelihood and time to go public, whereas prior literature using first-time IPO samples are able to test only the likelihood. The authors also show that the reputation of the advising PE firm attenuates the reluctance and time taken for RLBOs to re-list. The authors are, thus, able to provide a new perspective on the impact of SOX on the going public decision.

For India, with its low agricultural productivity and huge population, land acquisition has always been a serious policy challenge in the installation of land-intensive power projects. India has experienced a large number of projects getting stalled because of land conflict. Yet, there is a paucity of literature pertinent to India that tries to estimate future land requirements taking into consideration of land occupation metric.

In the present study, the dynamic land transformation and land occupation metrics of nine energy sources, both conventional and renewable, are estimated to further determine the magnitude of land requirement that India needs to prepare itself to fulfil its Intended Nationally Determined Contribution (INDC) commitments. This is illustrated through two different scenarios of energy requirement growth rates, namely, conservative and advanced.

This analysis suggests that, while nuclear energy entails the lowest dynamic land transformation when land occupation metric is taken into account, waste to energy source possesses least land requirement, followed by coal-fired source. Hydro energy source has highest requirement both in terms of dynamic land transformation and land occupation. It is also seen that land requirement will be 96% and 120% more in INDC scenario than business as usual (i.e. if India continues with its current share of renewables in its energy portfolio in 2030) considering a conservative and an advanced growth rate, respectively.

Some policy recommendations are provided that may aid policymakers to better address the trade-off between clean energy and land and incorporate it into policy planning. This study has not been able to consider future technical efficiency improvement possibilities for all energy sources, which can be incorporated in the proposed framework for further insight.

This paper provides a framework for estimation of future land requirement to fulfil India’s INDC energy plans which is not available in existing literature. The authors confirm that this manuscript is an original work.

The purpose of this paper is to assess the role of National Rural Health Mission (NRHM) in reducing Infant Mortality in India. The study will help the government in deciding its future course of action regarding the infant mortality rate (IMR) reduction in India.

This paper adopts the interrupted time series analysis (ITSA) approach with a control group to study the role of NRHM in reducing the IMR in India. The authors examined infant mortality in rural areas of India for the level and trend change before and after the implementation of NRHM. The authors then applied a suitable ARMA model to estimate the coefficients of the regression model. From the estimated results, the study predicts the counterfactuals for both the rural IMR and urban IMR and plots the results.

The study found the evidence supporting the hypotheses that the NRHM has led to a reduction in the difference between urban IMR and rural IMR. The research shows that the rural IMR declined at steeper rates in the post-NRHM period (2005–2015).

None of the existing studies analyses the impact of a social scheme like NRHM on the reduction of IMR in India by applying the ITSA. The study is unique as it estimates the counterfactuals and plots the results which show the impact of NRHM on reducing IMR.