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Investment in non-listed real estate funds (NREFs) in an emerging economy like India has its own challenges that entail a detailed understanding of the risks. The purpose of this paper is to identify the key risk factors across the life cycle of a NREF, based on a considered feedback of various real estate fund management stakeholders. It is important for the investors and fund managers to appreciate these risk factors to make informed investment decisions.

The present study based on the literature survey and discussion with experts identifies 39 risk attributes, which were further summarized using factor analysis into a smaller set of factors impacting NREF returns (risk). The relative importance of each risk attribute was examined and ranked using the relative importance index (RII). Further, cluster analysis using Euclidian distance was used to partition these risk attributes in various segments depending on their importance.

The risk attributes are summarized as five risk factors, i.e. regulatory RISK, foreign direct investment risk, entry risk, business risk and project risk. Whereas the top five perceived risk attributes are investee/partner risk, project entitlement risk, title risk, legislative and regulatory risk and project execution risk.

This study has significance to the industry practitioners and the academic community in developing an understanding of the dynamic nature of risks across the life cycle of the NREFs in India and classifying them at the macro-meso-micro levels.

This paper is one of the first attempts to understand the risks impacting NREFs in India. It will help investors develop a better strategic understanding of the risks across the life cycle of an investment.

Real estate forms an important part of any economy and the investment in real estate, in turn, is impacted by the macroeconomic environment of that country. The purpose of the present research is to examine macroeconomic determinants of foreign and domestic non-listed real estate fund (NREF) flows and to examine whether they are similar or different for an emerging economy like India.

The long and short-run cointegration between the time-series variables is estimated using the autoregressive distributed lag (ARDL) bounds test and error correction model (ECM) using quarterly data across the 2005–2017 period. ARDL is a suitable method for short time-series data.

The empirical results indicate that domestic NREF flows are positively and significantly impacted by real GDP and performance of listed real estate stocks (i.e. BSE realty index). Whereas, foreign NREF flows are positively and significantly impacted by the exchange rate, performance of listed real estate stocks and domestic NREF flows.

The empirical results have significant implications for academicians, policy makers and real estate market practitioners. In the context of these results, some interesting insights are gained that would help in the implementation of the policies aimed toward increasing the fund flows in the real estate sector, which in turn would have a significant trickle-down effect on the Indian economy.

The existing literature looks at macroeconomic and other drivers of foreign investment in international real estate investments. However, there are very few studies on the determinants of domestic real estate investment flows and on determinants of NREFs' investment flows; particularly in emerging markets. The present study, in contrast, evaluates simultaneously the macroeconomic determinants of the domestic and foreign NREFs' investment flows in India. The ARDL and ECM method used has been applied for the first time to the study of NREFs.

This case study aims to appraise the financial benefits of green building construction in developing countries. The case study presents, green building's positive net present value (NPV) investment in real terms and potentially enhanced stock market returns at the firm level compared to competitors.

The case study examines secondary data on a green building certification and longitudinal operation costs to estimate green building investments' financial benefits. The case study also compares the stock market performance of green building portfolio company with non-green building competitors of similar size and industry.

The case study finds out that the real return rate on green building investment is higher than the weighted average cost of capital (WACC) of the company with an inflation-adjusted payback period of fewer than ten years. Findings compare favourably to the extant literature which was mostly in developed economies. The paper further highlights that stock market performance for a green building focused company shows improved returns to shareholders relative to non-green competitors.

The results are specific to the time and building researched; green buildings costs have reduced over time, and a new study may show improved case study findings. The case study results on stock market performance are indicative and may need further research for evaluation.

The case study presents a model for critical appraisal of green buildings investment. The paper further indicates that green building investment may lead to operational savings and superior stock performance compared to competitors.

The paper presents a green building investment appraisal model which might be useful for the industry and academia. Developing countries have limited literature on green buildings' financial benefits; this case study quantifies the financial benefits and compares them with the available literature related to developed economies’ green buildings.

The purpose of this paper is to establish the importance of land records for urban development. The study focuses on how traditionally land records were managed and presently what are the important parameters impacting the land record management systems in India.

The framework adopted for the study was as follows: 1) literature study: aim to study the historical issues, to study global systems across the globe, various government reforms. 2) Present system of land management: to study the administrative, legal, economic issues, problems and potential in the present system. 3) Technology interventions: to study how technology can help to make the system more robust and trustworthy. 4) Conclusion: to study how the recommended technological measures will work and how to implement it in the system. Several pilot interviews were carried out to understand how the present system of land record management works in India, and important parameters were established through the pilot interviews of various stakeholders in the system.

The study brings out certain striking facts about the inefficiencies in the system since centuries, which are still being carried forward. Any reforms by the authorities have not been able to solve the issues and reduce the number of litigations because digitisation was only a step forward to replicate the wrong entries of records in digitised format. Thus, a paradigm shift in technology is required to bring a considerable change in the present management system.

Various studies worldwide have been done in several countries regarding land records, but all the studies are in piecemeal basis. Very less literature is available on the study that how land records effect large scale urban development projects. This study is an attempt to study impact of land records on urban development and to bring back transparency in the system to reduce the number of litigations on the most important ingredient of built environment, which is land.

This paper intends to contribute to the evolving understanding of Indian adolescents as consumers by examining their unique relationships with food brands, focusing specifically on brand love. It further investigates the key drivers that cultivate brand love among adolescents and explores the resulting outcomes of brand love.

About 37 in-depth interviews, including three exercises, were conducted with adolescents aged 11–16. The first and second exercises used projective techniques to explore respondents’ culture-bound love relationships with their favorite brands. Using the laddering technique, the third exercise investigated the critical drivers of respondents’ brand love.

The study reveals that adolescents derive value through attribute-benefit-value linkages from the consumption experience, leading to brand love. The customer value–brand love dynamics result in adolescents’ customer engagement behavior. Additionally, Indian adolescent customers seek brand consumption as a medium to instate their social identity and achieve hedonic pleasure from the experience. The study highlights the role of socialization and attitudinal autonomy in shaping adolescent–brand interactions.

The study could be relevant for both academicians and practitioners as they unveil the consumer psychology of contemporary adolescents in emerging countries like India and how similar or different they are from adult consumers. Also, there are very few adolescent–brand relationship studies in the past that have been deliberated in the context of food brands. Brand managers may design their product development and communication appeals around higher levels of abstraction in the attribute-benefit-value linkages discovered by this study.

In this chapter, I revisit an important debate about dalit feminism that took place in the pages of the Economic and Political Weekly, a leading publication in India, from 1995 to 2000 (Datar, 1999; Guru, 1995; Rege, 1998, 2000). Reexamining this debate in the context of contemporary dalit and savarna feminist activism, I show that while the debate was key in making visible (1) the heretofore unmarked savarna nature of autonomous feminism and (2) the male domination of dalit politics, in the decades following the debate, dalit politics remains primarily male, and autonomous feminism while cognizant of and in conversation with dalit feminism is not necessarily transformed by dalit standpoint. Further, dalit feminism itself while visible nationally and transnationally has focused at home largely on “difference,” from savarna feminism without adequately addressing the differences among dalit subjectivities in neoliberal India, limiting the possibilities of radical, coalitional politics.

The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.

In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.

The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.

Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in automated skin lesion analysis. The unavailability of adequate data poses difficulty in developing classification methods due to the skewed class distribution.

Boosting-based transfer learning (TL) paradigms like Transfer AdaBoost algorithm can compensate for such a lack of samples by taking advantage of auxiliary data. However, in such methods, beneficial source instances representing the target have a fast and stochastic weight convergence, which results in “weight-drift” that negates transfer. In this paper, a framework is designed utilizing the “Rare-Transfer” (RT), a boosting-based TL algorithm, that prevents “weight-drift” and simultaneously addresses absolute-rarity in skin lesion datasets. RT prevents the weights of source samples from quick convergence. It addresses absolute-rarity using an instance transfer approach incorporating the best-fit set of auxiliary examples, which improves balanced error minimization. It compensates for class unbalance and scarcity of training samples in absolute-rarity simultaneously for inducing balanced error optimization.

Promising results are obtained utilizing the RT compared with state-of-the-art techniques on absolute-rare skin lesion datasets with an accuracy of 92.5%. Wilcoxon signed-rank test examines significant differences amid the proposed RT algorithm and conventional algorithms used in the experiment.

Experimentation is performed on absolute-rare four skin lesion datasets, and the effectiveness of RT is assessed based on accuracy, sensitivity, specificity and area under curve. The performance is compared with an existing ensemble and boosting-based TL methods.

The purpose of this paper is to present an analytical study for a problem of unsteady free convection boundary layer flow past a periodically accelerated vertical plate with Newtonian heating (NH).

The equations governing the flow are studied in the closed form by using the Laplace transform technique. The effects of various physical parameters are studied through graphs and the expressions for skin friction, Nusselt number and Sherwood number are also derived and discussed numerically.

It is observed that velocity, concentration and skin friction decrease with the increasing values of Sc whereas temperature distribution decreases in the increase in Pr in the presence of NH.

This study is limited to a Newtonian fluid. This can be extended for non-Newtonian fluids.

Heat and mass transfer frequently occurs in chemically processed industries, distribution of temperature and moisture over agricultural fields, dispersion of fog and environment pollution and polymer production.

Free convection flow of coupled heat and mass transfer occurs due to the temperature and concentration differences in the fluid as a result of driving forces. For example, in atmospheric flows, thermal convection resulting from heating of the earth by sunlight is affected differences in water vapor concentration.

The authors have studied heat and mass transfer effects on unsteady free convection boundary layer flow past a periodically accelerated vertical surface with NH, where the heat transfer rate from the bounding surface with a finite heat capacity is proportional to the local surface temperature, and which is usually termed as conjugate convective flow. The equations governing the flow are studied in the closed form by using the Laplace transform technique. The effects of various physical parameters are studied through graphs and the expression for skin friction also derived and discussed.

Optimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.

Design of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.

In the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.

The originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.