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The purpose of this paper is to evaluate the influence of temperature, load and sliding speed on wear and friction behavior of LM25/SiC composites in as-cast and heat-treated conditions.

The LM25/SiC aluminum matrix composites (AMCs) were prepared using the stir casting process. The wear tests were carried out using a pin-on-disc setup in dry condition. The three levels of each parameter, i.e. 100, 150 and 200°C operating temperature; 15, 25 and 35 N load; 0.8, 1.6 and 2.4 m/sec sliding speed, were considered for the investigation. ANOVA has been carried out to evaluate the percentage contribution of parameters. Scanning electron microscope analysis of worn surfaces has been carried out to understand the wear mechanism.

The wear and coefficient of friction (COF) increase with the increase in the temperature, load and sliding speed within a selected range for as-cast as well as heat-treated LM25/SiC AMCs. The mean values of wear and COF in heat-treated samples were found to be lower than as-cast samples for all cases. It was observed that the percentage wear increases significantly as temperature increases in as-cast AMCS. Mild to severe wear transition was observed at 150°C. In heat-treated AMCs, mild wear was observed irrespective of temperature. It was also observed that as the temperature increases, transition of wear mechanism from abrasive to adhesive (including delamination) occurs earlier in as-cast samples as compared to heat-treated samples.

There is a lack of data on combined effect of temperature, load and sliding speed on tribological aspects of as-cast and heat-treated LM25/SiC AMCs, limiting its applications. The present research work has addressed this gap.

This study aims to assign efficiency score and then ranking the Indian companies known for best practices to control carbon-emission in the environment. It is destined to benchmark one company for best performance on the basis of selected alternatives among its peer group companies.

The present study has used a hybrid model by applying data envelopment analysis (DEA)-technique for order performance by similarity to ideal solution (TOPSIS) to measure the efficiency and ranking of various decision units on the basis of specified variables.

The findings of DEA have given the best alternative or best decision-making unit (DMU) among the set of 25 DMUs considered for empirical testing. The DEA technique is used with TOPSIS, which is another popular multi-criteria decision model. The integrated DEA-TOPSIS model has helped to compute the efficiency score of all 25 DMUs of study and also provide a unique rank to each of the efficient unit identified with the help of DEA technique.

The findings of the study have provided Benchmark Company amongst the companies following best practices for saving energy and having best operating profits too. This benchmark business unit can be studied extensively by peer group companies to compare various parameters affecting their efficiency and profits both.

The findings of the study will promote the socially responsible practices by corporate citizens and adopt the practices to reduce their carbon footprints. It will also suggest to socially responsible investors to select the benchmark and most efficient companies for investment purpose.

The study is original in terms of measuring efficiency and ranking of companies known for best practices for controlling their carbon footprints and suggesting a benchmark company to its peer group. Also, the integrated approach of using DEA-TOPSIS for such type of studies also makes it distinctive from earlier work done in the related field.

The existing literature on venture capitalists’ (VCs’) exits provides insufficient evidence regarding factors affecting the exit decision. This study aims to identify these factors and examine how VC firms do ranking or prioritize these factors.

The study is based on primary data. The qualitative analysis was done to develop the survey instrument. Fuzzy analytical hierarchical process, which is a popular method of multi-criteria decision modeling, is used to identify or rank the determinants of exit strategy by venture capital firms in India.

Broadly, eight determinants of exit strategy are ranked by VCs. A total of 33 statements describe these eight determinants. The results are analyzed on the basis of four measures of VCs’ profile, i.e. age of VC firm, number of start-ups in portfolios, type of investment and amount of investment.

The survey instrument needs to be validated with a larger sample size and other financial backers than VCs.

The study has direct managerial implication for VC firms as it provides useful information regarding the determinants of exit strategy by VC firms in India. These findings can provide necessary information to other financial backers too, viz., angel investors, banks, non-banking financial institutions and other individual and syndicated set-ups providing funding to start-ups.

The current research is unique as no prior study has explored the determinants of VCs exit strategy and prioritizing these determinants.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

In a wireless sensor network (WSN), the sensor nodes are distributed in the network, and in general, they are linked through wireless intermediate to assemble physical data. The nodes drop their energy after a specific duration because they are battery-powered, which also reduces network lifetime. In addition, the routing process and cluster head (CH) selection process is the most significant one in WSN. Enhancing network lifetime through balancing path reliability is more challenging in WSN. This paper aims to devise a multihop routing technique with developed IIWEHO technique.

In this method, WSN nodes are simulated originally, and it is fed to the clustering process. Meanwhile, the CH is selected with low energy-based adaptive clustering model with hierarchy (LEACH) model. After CH selection, multipath routing is performed by developed improved invasive weed-based elephant herd optimization (IIWEHO) algorithm. In addition, the multipath routing is selected based on certain fitness functions like delay, energy, link quality and distance. However, the developed IIWEHO technique is the combination of IIWO method and EHO algorithm.

The performance of developed optimization method is estimated with different metrics, like distance, energy, delay and throughput and achieved improved performance for the proposed method.

This paper presents an effectual multihop routing method, named IIWEHO technique in WSN. The developed IIWEHO algorithm is newly devised by incorporating EHO and IIWO approaches. The fitness measures, which include intra- and inter-distance, delay, link quality, delay and consumption of energy, are considered in this model. The proposed model simulates the WSN nodes, and CH selection is done by the LEACH protocol. The suitable CH is chosen for transmitting data through base station from the source to destination. Here, the routing system is devised by a developed optimization technique. The selection of multipath routing is carried out using the developed IIWEHO technique. The developed optimization approach selects the multipath depending on various multi-objective functions.

In this chapter, the problem of facial palsy has been addressed. Facial palsy is a term used for disruption of facial muscles and could result in temporary or permanent damage of the facial nerve. Patients suffering from facial palsy have issues in doing normal day-to-day activities like eating, drinking, talking, and face psychosocial distress because of their physical appearance. To diagnose and treat facial palsy, the first step is to determine the level of facial paralysis that has affected the patient. This is the most important and challenging step. The research done here proposes how quantitative technology can be used to automate the process of diagnosing the degree of facial paralysis in a fast and efficient way.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

The apparent differences between the leadership requirements of traditional and empowered environments suggest that traditional measures of leadership may not be applicable to empowered work environments. Through an exhaustive literature review and a series of in‐depth interviews with scientists in five national R&D labs in India, the purpose of this study is to develop a set of leader behaviors having high potential to impact creativity of R&D professionals.

A total of 52 interviews were conducted with scientists of five Indian R&D labs located in five different cities of India. The interview transcripts were content coded and a list of behavior items were generated. The list of items was given to five doctoral students to sort them into different behavior categories. Each behavior incident was coded using a modified version of the leader behavior taxonomy presented in the Managerial Practices Survey (MPS) (Yukl, Wall and Lepsinger).

Based on the consistency score, a final list of 52 behavior items representing five behavior metacategories was generated that has high potential of promoting employee creativity. A set of contextual variables was identified that can moderate the impact of leadership on employee creativity.

A large‐scale follow‐up survey would be useful to find outz which of the identified leader behaviors do indeed have the proposed connection with employees' creativity.

The identified behaviors can be of immense help to practitioners who often wrestle with the task of identifying appropriate behaviors that can ensure leader effectiveness.

This is the first study of its type and identifies a set of leader behaviors that can enhance creativity of R&D professionals.

The purpose of the chapter is to integrate the understanding of diversity from different perspectives in Indian context and see how the holistic view emerges.

The methodology used is primarily the literature review of the concepts and their evolution in Indian context and the use of secondary sources to extract praxis information.

It emerged from the exploration on diversity practices at the societal as well as organizational level in India that the country demonstrates intent to mainstream the people from different wakes, but with the changing context the format of the practices has changed.

The basic premise of the chapter needs to be explored further through primary data from practice.

This chapter is novel in a way that it integrates the diversity scholarship of four different streams viz. caste, gender, disability, and generation. Most of the existing research focuses only on a thin slice/one key dimension of diversity.

This study aims to examine the role of Bilateral Investment Treaties (BITs) in attracting higher foreign direct investment (FDI) inflows into the major emerging economies namely Brazil, Russia, India, China and South Africa (BRICS) from the source developed, developing and other emerging economies over a period of 18 years from 2001 to 2018.

To estimate the results, panel data regression on a gravity-knowledge capital model has been used. To account for the problem of endogeneity we have used the two-step difference Generalised Method of Moments estimator proposed by Arellano and Bond (1991).

We find that contradictory to theory and expectations, BITs result in a fall in FDI inflows in BRICS economies. BITs ratified by BRICS economies are not able to provide a sound and secure investment environment to foreign investors, thereby discouraging FDI in these economies.

To the best of the authors’ knowledge, this study is the first to examine the impact of BITs on FDI inflows into the emerging BRICS economies. Further, the impact of BITs on FDI flows among developed nations, i.e. north-north FDI and from developed to developing countries, i.e. north-south FDI has already been studied by many researchers. But so far, no study has examined this impact on FDI among developing and emerging economies (south-south FDI), despite an increase in FDI flows among these economies. Therefore, this study seeks to overcome the limitations of previous studies and tries to find out the impact of BITs on FDI inflows in BRICS economies not only from source developed but also from source developing and other emerging economies.