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304 SS substrates have been covered with polymeric class low melting barriers, like polyurethane, acrylate and epoxy with and without incorporation of eco‐friendly (non‐toxic) ceramic particular of antifouling origin. The results of corrosion resistance tests are encouraging. Other physical parameters like hardness, adhesion, scratch resistance are also studied, for these synergistically organized low melting point barrier layers.

This paper aims to investigate the wear behaviors of aged metal matrix composites and of the as-cast Al-Si alloy by using a pin-on-disk wear testing machine at room temperature.

Hypoeutectic (Al-7Si) alloy reinforced with low volume fractions of SiC particles (SiC_p) and graphite (Gr) particles were prepared by the stir-casting process. It was found that the addition of 9 Wt.% of SiC_p and 3 Wt.% of Gr particles conferred a beneficial effect in reducing the wear rate of the composites.

The worn-out surfaces of the specimens were examined using scanning electron microscopy (SEM); the extensive micro cracking occurs on the surface of the Al-7Si alloy tested at lower loads. The growth of these microcracks finally led to the delamination of the base alloy surface. The reinforcements (SiCp and Gr) particles tended to reduce the extent of plastic deformation in the surface layer, thereby reducing extensively the occurrence of micro cracking in the composites.

From the results, it is revealed that the quantity of wear rate was less for aged specimens compared to the as-cast specimens. The worn-out surfaces were studied using electron dispersive spectroscopy, and wear debris was analyzed using SEM.

This paper aims to analyse trends and determinants of NPAs in India's banks. It has empirically examined the bank-specific determinants of NPAs.

An FE panel estimation of a sample of 44 banks was carried out for the post-crisis time period, from 2010 to 2020 to identify the bank-specific determinants of NPAs. The sample of 44 banks includes 20 PSBs, 19 private banks and 5 foreign banks. Separate FE estimation was also carried out to identify the drivers of NPAs in PSBs.

The determinant of NPAs during the post-crisis period suggests that faulty earning management and deterioration in loan quality have resulted in high NPAs in India's banks. The result is similar for PSBs as well.

The findings of the study suggest that the banks, especially the Public Sector Banks (PSBs) need to revisit their earning management strategies to maximise income and improve their loan quality in order to reduce the incidence of loan failure.

The paper contributes by empirically analysing the determinants of NPAs during the recent decade, between 2010 and 2020. Separate estimations have been carried out to understand whether the drivers of NPAs differ in the case of PSBs.

The purpose of this paper is to evaluate the agility performance level of manufacturing supply chains using Taguchi loss functions (TLFs) and design of experiment (DoE).

The proposed methodology is used for capturing the various agility losses using appropriate TLFs and the aggregated agility loss is calculated at different situations using DoE. The aggregated agility loss is analysed for comparing manufacturing supply chain agility performance.

The proposed methodology was applied to three Indian auto component supply chains, i.e. X, Y and Z. In total, 27 experiments were carried out using DoE and obtained results show that agility performance level is the highest for X followed by Z, whereas agility performance level is the least for Y.

The proposed methodology is generic in nature and can be applied to a specific environment for comparing performance of different supply chains. The user has to identify the relevant agility enablers and capture the appropriate TLFs for the specific environment in which agility performance level has to be calculated and compared.

The proposed methodology provides an effective approach for evaluating agility performance. It can be used by the supply chain manger to assess the supply chain agility performance level of own company with its competitors. These comparisons will help the manufacturing company to find the areas where it should focus.

Many studies and researches related to implementation and evaluation of agile manufacturing are reported in the literature but very few studies are available for evaluating the supply chain agility performance. This study will definitely provide a guideline for measuring and comparing manufacturing supply chain agility performance in general and Indian automotive supply chain in specific.

The purpose of this study is to look at the multifaceted relationship mechanism between corporate social responsibility (CSR) and organizational performance (OP) via sustainability risk management (SRM) and organizational reputation (OR).

This research connects CSR to OP via SRM and OR. Based on a sample of 325 managers of multinational firms in India, a theoretical model was proposed and analyzed through sequential mediation regressions analysis.

The findings indicate that CSR is positively and appreciably associated with OP. Furthermore, SRM and OR have been found to have a sequentially mediating effect on the interrelationship between CSR and OP. The study recognizes that organizations with a proactive approach to CSR tend to manage sustainability risk more actively, which helps to improve OR and ultimately results in better OP.

The research advances understanding of the triple bottom line and offers a platform for building strategic and successful CSR policies by offering valuable insights on the link between CSR and OP.

The purpose of this paper is to examine the influence of ownership structure and dividend payouts over firm’s profitability, valuation and idiosyncratic risk. The authors further investigate if corporate performance is sector dependent.

The study employs signaling and bankruptcy theories to evaluate the influence of ownership structure and dividend payout over a firm’s corporate performance. The authors use a panel regression approach to measure the performance of family owned firms against that of widely held firms.

The study confines to firms operating out of emerging markets. The results show that family owned firms are dominant with concentrated ownership. The management pays lower dividend leading to lower valuation and higher idiosyncratic risk. The study further illustrates that family ownership concentration and family control both influence firm performance and level of risk. The findings indicate that information asymmetry and under diversification lead to increased idiosyncratic risk, resulting in the erosion of firm’s value. Results also confirm that firms paying regular dividends are less risky and, hence, command a valuation premium.

The evidence supports the proposition that information asymmetry plays a significant role in explaining dividend payouts pattern and related impacts on corporate performance. The originality of the paper lies in factoring idiosyncratic risk while explaining profitability and related valuation among emerging market firms.

To investigate the role of domestic and foreign economic policy uncertainty (EPU) in driving the corporate bond yields in emerging markets.

The study utilizes monthly data from January 2008 to June 2023 from the selected emerging economies. The data analysis is conducted using univariate, bivariate and multivariate statistical techniques. The study includes bond market liquidity and global volatility (VIX) as control variables.

Domestic EPU has a significant role in driving corporate bond yields in these markets. The study finds weak evidence to support the role of the USA EPU in influencing corporate bond yields in emerging economies. Domestic EPU holds more weight and influence than the EPU originating from the United States of America.

The findings provide useful insights to policymakers about the potential impact of policy uncertainty on corporate bond yields and enable them to make informed decisions regarding economic policies that maintains financial stability. Understanding the relationship between EPU and corporate bond yields enables investors to optimize their investment decisions in emerging market economies, opens the scope for further research on the interaction between EPU and volatility and other attributes of fixed income markets.

Focuses specifically on the emerging market economies in Asia, providing an in-depth analysis of the dynamics and challenges faced by these countries, Explores the influence of both domestic and the USA EPU on corporate bond yields in emerging markets, offering valuable insights into the transmission channels and impact of EPU from various sources.

The car no longer serves simply as a means of transport but is at the core of a new concept of mobility. Car manufacturers are seizing opportunities to change the traditional business model of the auto business. Innovation in this business model has become vital to survival in today’s dynamic market conditions. This paper aims to find out what factors motivate and drive business model change and what the resulting business model innovation is.

This qualitative study is based on a single case, namely, BMW as an illustrative example of an advanced, highly innovative customer-centric service business model (BM). The study adopts a document analysis method to reveal the firm’s BMI process.

First, the study presents a conceptual framework for business model change with the factors –motivators and drivers – that impact on the process of change. BMW’s BMI and its impacting factors are discussed based on this model. The McKinsey 7 s Model framework, the elements of which are strategy, structure, systems, shared values, style, staff and skills is used as an analytical tool to discuss new business model implementation. The study highlights the BM configuration of a traditional car manufacturer, the car as a product and the new car as a service concept.

This study reveals the BMI of BMW’s digital services and its key motivators and drivers. BMW mostly innovates in three key dimensions of the Business model. These are value creation, value delivery and value capture. Most of the elements in these dimensions are innovated.

The purpose of the paper is to investigate the global and regional influences on the domestic term structure of nine Asian economies.

The dynamic Nelson Siegel model was used to extract the latent factors of a country’s yield curve movements in a state-space framework using the Kalman filter. The global and regional factors of the yield curve were extracted using the dynamic factor model. Further, the Bayesian inference of Gibbs sampling approach was used to identify the influence of global and regional factors on the domestic yield curve.

The results suggest that financial integration does not reduce the control of monetary authorities on the front end of the yield curve, and long-term interest rate is the potential transmission channel through which the contagion of the financial crisis spreads.

The results of this study would help the monetary authorities to understand the efficacy of the monetary policy transmission mechanism. It also offers the global investors diversification opportunities for investing in the Asian bond markets.

It is one of the earliest attempts to capture the global and regional yield curve movements and their impact on the emerging Asian economies yield curve. It contributes to literature by identifying the linkages in the long-term factor that is the potential channel through which crisis spreads.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.