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The purpose of this paper is to develop chicken cutlets enrobed with bread crumbs vis-à-vis dried carrot pomace and to assess its effect on physico-chemical properties, sensory attributes and texture profile analysis.

Three experimental groups were made: control group chicken cutlets (C), chicken cutlets enrobed with bread crumbs group (T_b) and chicken cutlets enrobed with dried carrot pomace group (T_c). All the procedures used in the study for estimation of various physico-chemical properties, sensory evaluation and texture profile analysis were standard protocols.

There was a significant (p < 0.05) increase in water holding capacity, crude fibre content and ash content of enrobed chicken cutlets, whereas moisture, fat content and shrinkage of product were significantly (p < 0.05) decreased. The results for sensory evaluation and texture profile analysis of enrobed chicken cutlets were better than control group. Overall acceptability score of chicken cutlets enrobed with dried carrot pomace was revealed to be highest (7.5 ± 0.29) and that of control group was found to be lowest (6.4 ± 0.22). Hardness (N/cm²) value found for control group chicken cutlets, chicken cutlets enrobed with bread crumbs group and chicken cutlets enrobed with dried carrot pomace group were 2.2 ± 0.17, 3.1 ± 0.29 and 4.3 ± 0.27, respectively.

Future research may benefit to assess the effect of enrobing with bread crumbs and dried carrot pomace on mineral and vitamin content and lipid profile of meat products.

Enrobing of chicken cutlets with bread crumbs and dried carrot pomace improved the sensory attributes along with texture profile analysis. Hence, enrobing with bread crumbs and dried carrot pomace could be used as processing technology to improve sensory appeal, especially crispiness of meat products.

The purpose of this study is to produce spent hen tikka of improved quality attributes using lemon juice and ginger extract marination.

Three experimental groups were made: control group, 20 per cent lemon juice marinated group (LM) and 50 per cent ginger extract marinated group (GM). Boneless spent hen breast meat was cut into small cubes of one inch with the help of knife and kept in marinade solution in ratio of 2:1 w/v at 4 ± 1°C for 16 hours in a refrigerator. Chicken tikka was prepared using an electric oven at the temperature of 240°C for 20 minutes.

There was a significant (p < 0.05) increase in moisture content and water holding capacity of LM and GM marinated chicken tikka, whereas protein, fat, ash, cholesterol content and shear force values were significantly (p < 0.05) decreased. pH was significantly (p < 0.05) higher in GM and significantly (p < 0.05) lower in LM compared to control for chicken tikka.

Future research may be carried out to assess the effect of lemon juice and ginger extract marination on mineral content and lipid profile.

Marination of meat with LM and GM improved the sensory scores and textural properties, whereas fat and cholesterol content of chicken tikka decreased. Therefore, marination of chicken tikka with LM and GM may be used as processing technology to improve quality attributes of spent hen tikka.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

Some studies have been reported in the past on diaphragmatic hernia (DH) surgery techniques using additive manufacturing (AM) technologies, symptoms of a hernia and post-surgery complications. But hitherto little has been reported on bibliographic analysis (BA) for health monitoring of bovine post-DH surgery for long-term management. Based on BA, this study aims to explore the sensor fabrication integrated with innovative AM technologies for health monitoring assistance of bovines post-DH surgery.

A BA based on the data extracted through the Web of Science database was performed using bibliometric tools (R-Studio and Biblioshiny).

After going through the BA and a case study, this review provides information on various 3D-printed meshes used over the sutured site and available Internet of Things-based solutions to prevent the recurrence of DH.

Research gaps exist for 3D-printed conformal sensors for health monitoring of bovine post-DH surgery.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

Fused deposition modelling (FDM) has gained popularity owing to its capability of producing complex and customized profiles at relatively low cost and in shorter periods. The study aims to extend the use of FDM printers for 3D printing of low melting point alloy (LMPA), which has applications in the electronics industry, rapid tooling, biomedical, etc.

Solder is the LMPA with alloy’s melting temperature (around 200°C) lower than the parent metals. The most common composition of the solder, which is widely used, is tin and lead. However, lead is a hazardous material having environmental and health deteriorating effects. Therefore, lead-free Sn89Bi10Cu non-eutectic alloy in the form of filament was used. The step-by-step method has been used to identify the process window for temperature, print speed, filament length (E) and layer height. The existing FDM printer was customized for the present work.

Analysis of infrared images has been done to understand discontinuity at a certain range of process parameters. The effect of printing parameters on inter-bonding, width and thickness of the layers has also been studied. The microstructure of the parent material and deposited bead has been observed. Conclusions were drawn out based on the results, and the scope for the future has been pointed out.

The experiments resulted in the process window identification of print speed, extrusion temperature, filament length and layer height of Sn89Bi10Cu which is not done previously.

The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.

Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.

It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.

This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.

Digital technologies emerged as innovative avenues for launching new products, advertising brands, increasing customer awareness and thus leaving a remarkable impact on the online marketplace. The present study analyzed the effects of crucial antecedents of AR interactive technology on customers' behavior toward AR-based e-commerce websites.

Convenience sampling was used to collect primary data from 357 iGen respondents aged 16–22 years; residing in New Delhi and the NCR region of India and examined using the structural equation modeling technique.

Results revealed that technology anxiety and virtuality significantly influence customers' attitudes and behavioral intentions toward AR-based e-commerce websites. However, interactivity and innovativeness remain non-significant. Additionally, non-significant moderating effects were identified for the moderators, i.e. trust and need for touch. At the same time, gender has a significant moderating effect only for the association between technology anxiety and attitude toward AR-based e-commerce websites.

The study summarizes numerous theoretical and managerial implications for AR-based website designers and policymakers, followed by the crucial limitations and directions for future research.

The present research provides a significant understanding of the e-commerce industry by providing valuable insights about young iGen consumers' perceptions of AR-based e-commerce websites.

The purpose of this paper is to propose a conceptual framework for the application of blockchain in the Public Distribution System (PDS) in India to manage the supply of food grains to the targeted beneficiaries. The framework will help prevent diversions and leakages of grains at the warehouse and Fair Price Shop (FPS) level. The paper also identifies the enablers and disablers in the context of the framework.

This paper will firstly review the previous literature in PDS and blockchain-enabled agricultural and food supply chains. The study then proposes a framework that could be implemented in the PDS in India using blockchain technology.

The proposed framework provides an effective way to combat corruption, exclusion errors of targeted beneficiaries, leakage of PDS food grains and is cost-effective. The identified enablers and disablers give an insight into the application of blockchain in PDS in India.

The research work may have implications for the Ministry of Food and PDS (Central Government), Food Corporation of India and State Governments to manage the supply of the grains more efficiently and effectively.

The current study caters to the implementation of blockchain technology starting from the warehouse level to the FPSs and consumers and simultaneously connecting them to concerned authorities to ensure transparency and accountability.

There is a lacuna in research work in terms of understanding how Indian IT organizations can become global brands. Benchmarking has not received much attention in marketing literature due to lack of benchmarking framework, and IT organizations are yet to make progress in benchmarking. The purpose of this paper is to examine the impact of brand strength on global branding by developing a conceptual benchmarking framework for Indian IT organizations.

Semi-structured in-depth interviews are conducted with thirty middle-level managers from two Indian IT organizations, two US-based global IT organizations and one UK-based leading bank, which is a customer of these IT organizations.

Results show a positive relationship between brand strength and global branding, between customer loyalty and global branding, between brand loyalty and competitive advantage and between global branding and competitive advantage. Indian IT organizations can benchmark global IT organizations to improve delivering brand promise, positioning, awareness building and authenticity toward making Indian IT organizations future ready to address the entire breadth of opportunities in the evolving world of cloud and digital.

This research helps managers with a brand strength-based benchmarking framework toward global branding of Indian IT organizations.

IT is instrumental for rapid growth of Indian’s economy. India should optimally utilize its greatest wealth, its human potential, with the latent global demand in IT through building global IT brands.

The originality of the study lies in conducting a qualitative study on global branding of Indian IT organizations and also proposing a conceptual benchmarking framework. The study further validates the model using qualitative analysis.