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This study aims to explore the impact of “Performance Expectancy” (PE) on the performance of Indian automotive manufacturing employees through their “Behavioural Intention” (BI) in the context of digital learning within the volatile, uncertain, complex and ambiguous (VUCA) world.

A descriptive research design was adopted to gather data from 211 employees of Indian automotive manufacturing companies. Structural equation modelling was applied for data analysis and obtaining results.

Largely, the findings indicate that BI does mediate the relationship between PE and employee performance (EP) in the context of the Indian automotive manufacturing sector. The findings would help in boosting digital learning initiatives, improving EP and fostering organizational success in the VUCA world.

The current study focused on the Indian manufacturing industry. Extending the research beyond the manufacturing sector to other industries in India could possibly help in generalizing the findings, and thereby enhance knowledge of the broader consequences of digital learning on EP.

Digital learning platforms can enhance sustainable industrialization in the manufacturing sector by providing employees with access to digital learning opportunities. Managers must provide employees with access to digital learning opportunities for making them more creative and innovative, so that they can become real change agents, and can handle real-world problems more efficiently, leading the organization to deal with the complexities and unpredictability in this VUCA world.

Within the ambits of Indian automotive manufacturing organizations, there have been limited research studies that have used two constructs of the UTAUT2 framework to see their impact on EP in terms of digital learning. Thus, based on the limited information available, this research stands as a pioneering effort in the realm of digital learning within the Indian automotive manufacturing industry, where the relationship between PE and EP is examined through the mediational role of BI of employees. Moreover, this research possibly would pave the way for further research studies on exploring the role of potential factors influencing the strength and direction of the relationships between these variables (PE and EP).

This study examines students’ perspectives towards the utilization of information and communication technology (ICT), during this sudden shift to remote online education due to COVID-19 worldwide pandemic. The aim is to identify the predictors of learning outcomes and understand if they are here to exist as the new normal.

The independent variable motivation, managing emotion, and acceptability of ICT, are examined as potential determinants of perceived learning outcomes in remote online education. An aggregate of 220 responses from the students of management graduates in higher education were collected to examine the predictors of learning outcomes using regression model in SPSS software. In addition, ANOVA technique was used to compare and assess managing emotion, motivation, and ICT acceptability of male and female students in remote online education.

Results indicate that motivation, managing emotion and acceptability of ICT are significant predictors, which affect students’ perceived learning outcomes. Furthermore, the study reveals that managing emotions and motivation levels of female students are higher than male students in remote online education.

Research identifies the antecedents of student learning outcomes in management education. These finding may be useful for educators and management to understand the factors influencing students' learning outcomes and to develop various modules to make remote online learning effective.

This research contributes significantly in investigating the antecedents of students learning outcome and provide insights regarding student’s perspective towards sudden shift to remote online education due to COVID-19 worldwide pandemic.

Pearl millet (Pennisetum glaucum) is a rich source of nutrients as compared to the major cultivated cereal crops. However, major factors which limit its utilization are the presence of anti-nutritional factors (phytate, tannins and polyphenols) which lower availability of minerals and poor keeping quality because of higher lipase activity. Therefore, this paper aims to focus on the impact of different processing methods on the nutrient composition and anti-nutritional components of pearl millet.

This is a literature review study from 1983 to 2017, focusing on studies related to pearl millet processing and their effectiveness in the enrichment of nutritional value through reduction of anti-nutritional compounds.

From the literature reviewed, pearl millet processing through various methods including milling, malting, fermentation, blanching and acid as well as heat treatments were found to be effective in achieving the higher mineral digestibility, retardation of off flavor, bitterness as well as rancidity problems found during storage of flour.

Through this review paper, possible processing methods and their impact on the nutrient and anti-nutrient profile of pearl millet are discussed after detailed studied of literature from journal articles and thesis.

Diabetes is a chronic disease and the major types of diabetes are type 1 and type 2. On aging, people with diabetes tend to have long-term problems in hypertension, coronary artery disease, obesity, and nerves. Given the increasing number of complications in recent years, by 2040, 624 million people will have diabetes worldwide and l in 8 adults will have diabetes in the future. Machine learning (ML) is evolving rapidly, many aspects of medical learning use ML. In this study, tension-type headaches (TTH) were associated with diabetes using SPSS, Pearson correlation, and ANOVA tests. Data were collected from Delhi NCR Hospital. It contains 30 diabetic subjects. The purpose of this study was to correlate diabetes analysis from TTH and other diseases using the latest technologies to analyze the Internet of Things and Big Data and Stress Correlation (TTH) on human health. The authors used Pearson correlation to correlate study variables and see if there was any effect between them. There was an important relationship between the percent variable, the total number of individuals, the number of individuals, and the minimum variable. The age (field) of the number of individuals to one of the total number of individuals showed a strong correlation (1.000) with a significant value of p (1.000). Overall, cases of TTH increased with age in men and do not follow the pattern of change in diabetes with age, but in cases of TTH, patterns of headaches such as diabetes increase to age 60 and then tend to decrease.

The aim of this study is to provide a review of the research studies that took place in the previous years regarding Mangiferin and its potential use in the treatment of various disorders such as diabetes, cardiovascular diseases, cancer and human immunodeficiency virus.

Mangiferin can be easily extracted from the plant at a very low cost to treat some common as well as deadly diseases, which will be a miracle in herbal treatment. In support of this, the recent related articles were considered to understand the bioactive compound “Mangiferin” to treat lifestyle diseases. Various review articles and research papers were collected from international and national journals, and internet sources were also being used to compile the current manuscript.

Mangiferin has been shown to have the potential to mitigate many life style diseases such as diabetes, obesity, hypertension, fatty liver, atherosclerosis, dyslipidemia and diabetes-related end-organ complications such as retinopathy, neuropathy and nephropathy.

In this comprehensive review, this study evaluates Mangiferin and its lifestyle disease-modifying properties for nutraceuticals purposes.

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

The paper focused onto the development of microcapsules by using two essential oils. It proposes the uses of eucalyptus oil and cedarwood oil as a natural insecticide. The purpose of this paper is to demonstrate the application of developed microcapsules to impart insect repellency on textile substrate.

The paper opted for an experimental study using two essential oils and gum in formations of microcapsules through a simple coaseravtion encapsulation technique. The developed solution was analyzed, including confirmation of size and structure through. Application of developed finish on substrate was also undertaken to prove better ability as repellent fabric.

The paper highlights useful invention of microencapsulated fabric developed with the combination of gum acacia and eucalyptus oil as core and shell material. The developed fabric has better ability to repel silverfish as compared to microencapsulated fabric developed with gum acacia (shell) and cedarwood oil (core).

Due to the lack of time and less availability of essential oils, only two oils were used to test the insect repellent behavior.

This paper fulfills an identified need, it includes implications for the development of a very useful natural insecticide to repel silverfish (Lepisma saccharina) insect. This insect is a very common problem found in cloth wardrobes and bookshelves; it mainly attacks the fabric with cellulosic content and starch.

Society will get major benefit of using these microencapsulated finished fabrics, which repel silverfish from their home and keep their clothing and books safe for longer period. The natural fragrance and medicinal benefits of these essential oils can never be ignored.

This study sets a new approach to repel insects like silverfish from the bookshelves and clothing wardrobes. A layer of insect repellent microencapsulated finished fabric can be added in these shelves and wardrobes. It is an eco-friendly approach of using natural essential oils instead of chemical insecticides.