Indian Case Research Articles

Development of end-to-end low-cost IoT system for densely deployed PM monitoring network: an Indian case study

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

Stock return, volatility in an emerging market: The Indian case

Stock return, volatility in an emerging market: The Indian case

Non-discretionary output oriented data envelopment analysis for ranking airports: an Indian case study

Non-discretionary output oriented data envelopment analysis for ranking airports: an Indian case study

Demographic implications on Sustainable Consumption: An Indian case study.

Sustainable consumption refers to a mind-set where the consumers adopt environmentally and socially responsible consumption behaviour with the focus on meeting one has needs and simultaneously reducing its impact on society and the environment at large. Literature review suggests that sustainable consumption is a very flexible concept that incorporates various factors and these factors can differ among consumers. As a result, this paper aims to study if consumers with different demographics in India differ in sustainable consumption behaviour. Specifically, consumers of different genders, age groups, and income groups differ in their consumption behaviour due to factors such as Quality of Life, Environmental Concerns, and Care for Future Generations. Quantitative research was undertaken for this study and data was collected through a structured questionnaire from 281 samples selected via a non-probability sampling method. The data was analysed using ANNOVA (normally distributed data) and Mann Whitney U Test/Wallis H test (not normally distributed data). Findings indicate that men and women significantly differ in their consumer behaviour on factors of quality of life, care for the environment, and care for future generations. Quality of life also significantly affects sustainable consumption behaviour among the various income groups and age groups. These findings therefore can in the future support marketers in adopting various sustainable practices according to the importance attached to their target audience.

Non-discretionary output oriented data envelopment analysis for ranking airports: an Indian case study

Non-discretionary output oriented data envelopment analysis for ranking airports: an Indian case study

Understanding the Role of growth Drivers in Agri tech Startup Ecosystem: Experiences and Learning from Indian Cases

"Increased start-up activity is beneficial to agricultural sector in addressing the bottlenecks in agriculture and as well empowering the stakeholders of agri value chain. In India, the start-ups working in the agricultural sector are confronted with two scenarios. One is, they account for only 7.37 % of total startups and the other is, only around 10.05 % are able to progress to scaling stage while more than 50 % are in their early stages of their life-cycle. Thus, the present study aims to understand the key drivers essential for agri start-ups to progress quickly through business life-cycle stages through a multi case study approach. As B2B is emerging as key revenue generating segment of agri tech start-ups, three agri tech start-ups in this segment having similar line of business and with common competition i.e., traditional agricultural markets are considered for the study. The findings indicated that higher education levels, expertise of managerial team and technological innovation are growth drivers through the ideation, validation and early traction stage. Strategic drivers namely vision, market-oriented opportunity switch, networking and market expansion are playing a key role in growth of identified start-ups. The short term and long-term strategic decisions pertaining to these strategic drivers derived out of day-to-day experiences and learning’s are major growth drivers in pushing the start-ups towards growth stage. While business strategies for achieving scalability, gaining investor confidence are vital growth drivers to attain scale-up stage. An improvement in operating revenues and assets derived out of growth factors is reflected in financial progress."

Do trade and technology foster employment growth? Evidence from Indian industries

AbstractSeveral developing economies have been generating fast growth, but formal employment generation remains a concern, causing a significant workforce to continue working in the informal sector. In this context, this study investigates the employment effects of international trade and in‐house R&D in the Indian case. Our attempt is important because India is not only the largest country in terms of population but also the largest working population in the world. We coalesce industry‐level data from two different sources for the period 1991–2018 and employ a powerful system GMM estimator in the analysis. Our findings are interesting and vital. We show that although the role of trade is overall positive, export and import have contrasting roles individually in generating employment. Most generally, exporting is a generator of employment, while importing causes a loss in it. Our analysis implies that the interplay of trade, and individually that of exports and imports, with in‐house R&D depends on many factors and may be more intricate than previously assumed.

Legal Implications of Ai Navigating Patent Law Challenges in the Age of Ml

This paper examines the legal ramifications of artificial intelligence (AI) in the field of patent law, with a specific emphasis on practical uses and technological benefits. It emphasizes the significance of detecting and eliminating interdependencies among data items in the intricate domain of AI and ML, facilitating concurrent processing, and averting periods of inactivity. The progress of AI has had a substantial impact on patent law, resulting in the creation of laws specifically tailored to AI-related patents. The paper examines the difficulties associated with obtaining patents for AI-related discoveries, with a specific focus on issues related to identifying the inventors and the eligibility of the patents. The study explore issues pertaining to intellectual property, questions about prejudice and fairness, and the need for regulation and governance. The text emphasizes the significance of copyright, patents, trade secrets, and data privacy in safeguarding the progress made in AI and ML. The paper highlights the significance of regulatory frameworks that effectively manage the balance between innovation and user safety. It draws upon ideas from Indian case law and the proposed AI regulatory framework

Caregivers Journey Through Experiences of People Living with Dementia and History of Wandering Behaviour: An Indian Case Series

Caregivers Journey Through Experiences of People Living with Dementia and History of Wandering Behaviour: An Indian Case Series

Whole exome sequencing identifies novel variants of PIK3CA and validation of hotspot mutation by droplet digital PCR in breast cancer among Indian population

BackgroundBreast cancer (BC) is the most common malignancy with very high incidence and relatively high mortality in women. The PIK3CA gene plays a pivotal role in the pathogenicity of breast cancer. Despite this, the mutational status of all exons except exons 9 and 20 still remains unknown.MethodsThis study uses the whole exome sequencing (WES) based approach to identify somatic PIK3CA mutations in Indian BC cohorts. The resultant hotspot mutations were validated by droplet digital PCR (ddPCR). Further, molecular dynamics (MD) simulation was applied to elucidate the conformational and functional effects of hotspot position on PIK3CA protein.ResultsIn our cohort, PIK3CA showed a 44.4% somatic mutation rate and was among the top mutated genes. The mutations of PIK3CA were confined in Exons 5, 9, 11, 18, and 20, whereas the maximum number of mutations lies within exons 9 and 20. A total of 9 variants were found in our study, of which 2 were novel mutations observed on exons 9 (p.H554L) and 11 (p.S629P). However, H1047R was the hotspot mutation at exon 20 (20%). In tumor tissues, there was a considerable difference between copy number of wild-type and H1047R mutant was detected by ddPCR. Significant structural and conformational changes were observed during MD simulation, induced due to point mutation at H1047R/L position.ConclusionsThe current study provides a comprehensive view of novel as well as reported single nucleotide variants (SNVs) in PIK3CA gene associated with Indian breast cancer cases. The mutation status of H1047R/L could serve as a prognostic value in terms of selecting targeted therapy in BC.

Energy analysis of high-rise residential buildings under demolition using controlled explosion: An Indian case study

The end-of-life phase of buildings has not found adequate representation in the literature despite affording opportunities for reclaiming precious construction material and energy savings from the reuse and recycling of demolition waste. Moreover, the extant literature has limited the research focus primarily to the energy analysis of only the building portion, while the associated components, such as mechanical, electrical and plumbing (MEP) services and site features, are generally excluded from study scope. Furthermore, the majority of research studies in this area have excluded the demolition of buildings using controlled explosion. The current study aims to address these research gaps with a view to estimate the demolition energy requirements for the prospective demolition of a recently constructed high-rise residential building, including all connected services and site features. The demolition is executed using a hybrid method comprising deconstruction, mechanical demolition, and controlled explosion.The results indicate that, although building-related sub-systems accounted for approximately 80% of demolition waste and demolition energy requirements, non-building sub-systems, including MEP services, represented a considerable share of about 20% and thus should not be ignored. The overall waste generation rate of the building project is estimated to be about 1715 kg/m2.Concrete contributed to the highest proportions of demolition waste (73.20%) and demolition energy consumption (84.46%), while steel scrap enabled the highest amount of embodied energy recovery (90.46%) in recycling. Overall, the demolition project resulted in the recovery of nearly 2.70% of the embodied energy consumed during the pre-occupation stage of the building project life cycle.The findings of this study are expected to be useful to stakeholders in identifying opportunities to reduce the demolition energy footprint to make the end-of-life stage of a building project more sustainable. The study also establishes the need for emergent policy initiatives on the revision of guidelines for demolition waste management based on the realistic waste generation rate of high-rise residential buildings; the establishment of waste exchanges to reduce landfill space requirements; and incentivizing the use of recycled products in new constructions. Additionally, some relevant areas have been suggested for further research.

Survival outcomes for lung neuroendocrine tumors in California differ by sociodemographic factors.

Lung neuroendocrine tumors (NETs) have few known predictors of survival. We investigated associations of sociodemographic, clinicopathologic, and treatment factors with overall survival (OS) and lung cancer-specific survival (LCSS) for incident lung NET cases (typical or atypical histology) in the California Cancer Registry (CCR) from 1992 to 2019. OS was estimated with the Kaplan-Meier method and compared by sociodemographic and disease factors univariately with the log-rank test. We used sequential Cox proportional hazards regression for multivariable OS analysis. LCSS was estimated using Fine-Gray competing risks regression. There were 6038 lung NET diagnoses (5569 typical, 469 atypical carcinoid); most were women (70%) and non-Hispanic White (73%). In our multivariable model, sociodemographic factors were independently associated with OS, with better survival for women (hazard ratio (HR) 0.62, 95% confidence interval (CI) 0.57-0.68, P < 0.001), married (HR 0.76, 95% CI 0.70-0.84, P < 0.001), and residents of high socioeconomic status (SES) neighborhoods (HRQ5vsQ1 0.73, 95% CI 0.62-0.85, P < 0.001). Compared to cases with private insurance, OS was worse for cases with Medicare (HR 1.24, 95% CI 1.10-1.40, P < 0.001) or Medicaid/other public insurance (HR 1.45, 95% CI 1.24-1.68, P < 0.001). In our univariate model, non-Hispanic Black Californians had worse OS than other racial/ethnic groups, but differences attenuated after adjusting for stage at diagnosis. In our LCSS models, we found similar associations between sex and marital status on survival, but no differences in outcomes by SES or insurance. By race/ethnicity, American Indian cases had worse LCSS. In summary, beyond disease-related and treatment variables, sociodemographic factors were independently associated with survival in lung NETs.

A critique of the national asset reconstruction company, India’s bad bank

PurposeThis study aims to provide a critique of the institutional framework of the National Asset Reconstruction Company Limited (NARCL), India’s “bad bank.”Design/methodology/approachThe approach followed is the development of an economic model of the gains to trade of an Asset Reconstruction Company.FindingsUsing an economic model, the paper shows that the rules of the game create an uneven playing field and are likely to lead to a systematic overpricing of nonperforming loans (NPLs). While this might increase the upfront cash received by the bank, it will come at the cost of higher liabilities for the government on account of the sovereign guarantee that is part of the proposed structure. The overpricing is also likely to act as an obstacle to efforts aimed at the revival of the distressed company. The NARCL could become a warehouse for NPLs subverting the objective of reconstruction.Practical implicationsSolutions to create a vibrant market for NPLs are proposed.Originality/valueThe Indian case is unique in the sense that a bad bank backed by the government has been introduced after almost two decades of the existence of similar entities in the private sector. To the best of the author’s knowledge, this paper is the first that addresses the design of such a market using economic modeling.

Examining Occupant-Comfort Responses to Indoor Humidity Ratio in Conventional and Vernacular Dwellings: A Rural Indian Case Study

Optimum indoor humidity is often associated with comfort and overall well-being. Occupant comfort is often evaluated with a focus on “thermal comfort” using the PMV (predicted mean vote), PDD (predicted percentage of dissatisfied), and adaptive thermal comfort models. Humidity-determined comfort parameters, like skin and respiratory comfort, are well acknowledged in the scientific community, but strangely not considered for indoor comfort computations. This study proposes a new computational approach for describing and evaluating humidity-related skin comfort in buildings using skin temperature, evaporative loss, and skin wettedness as critical parameters. The Development and validation of the computational model was demonstrated through a case study in a rural Indian context. The case study involves real-time monitoring of indoor environmental parameters and humidity-determined occupant comfort votes recorded through a novel aggregated humidity comfort vote method. The simulation results were compared with the community comfort/health survey. It was observed that, even at neutral skin temperatures, an increase in skin wettedness increases the thermal sensation vote. Clothing varies according to gender, community, and personal preferences, influencing physiological parameters which determine comfort. The acceptable humidity ratio was found to be in the range of 17.4 to 22.6 g-wv/kg-da for Indian participants. Including humidity-related comfort parameters in building simulation tools would aid in selecting building materials for improved indoor comfort.

Modeling climate change impacts on vector-borne disease using machine learning models: Case study of Visceral leishmaniasis (Kala-azar) from Indian state of Bihar

Visceral leishmaniasis or Kala-azar (KA) is a Vector-Borne Disease (VBD) that remains the second-largest parasitic killer across the globe (mortality rate: 75–95%). More than 60% of KA cases originate in South Asia, wherein India accounts for 2/3rd of the cases, and Bihar, a state in India, alone accounts for more than 50% of the Indian cases. Past studies suspected climate change vulnerabilities as a driving cause of KA outbreaks. The VBDs-based epidemic prediction systems have been developed to mitigate recurrent outbreaks; however, Machine Learning (ML) based approaches still need to be explored for modeling changing climate impacts on KA cases. This study, for the first time, develops a Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR), hereinafter RBF-kernel-based-SVR model for the most-affected endemic districts of Bihar (northern-India), using the data from 2016 and 2021. Forward selection, backward elimination, and stepwise regression procedures were adopted while selecting influential climatic variables, followed by the k-fold cross-validation technique and, then, the RBF-kernel-based-SVR algorithm for classification. Results suggested that temperature, wind speed, rainfall, and population density significantly contributed to the KA outbreaks. This study also developed Multiple Linear Regression (MLR) and Multilayer Perceptron (MLP) models to compare SVR with other classification models. Findings indicated that the proposed RBF-kernel-based-SVR model [Correlation Coefficient (CC) = 0.82, Root-Mean-Square Error (RMSE) = 12.20, and Nash–Sutcliffe Efficiency (NSE) = 0.66] outperformed MLR (0.81, 14.20, 0.48) and MLP (0.81, 12.95, 0.61). Study recommends using the RBF-kernel-based-SVR model as a quick and efficient model capable of detecting KA cases with high predictability even under limited data availability. Such models can assist public health authorities, given monitoring KA spread, learning the climate impacts of outbreaks, and ensuring timelier health services.

Site suitability analysis for locating construction and demolition waste transfer station: an Indian case study.

Construction and Demolition Waste Management (CDWM) includes collecting, transporting, processing, and disposing construction and demolition (C&D) waste, where collection and transportation of bulky and voluminous C&D waste contribute significantly to economic and environmental impacts. Transfer station (TS) being a link between various waste management (WM) facilities plays a significant role in collection and transportation of waste. Thus, locating TS at suitable site can help in reducing the overall impacts. Employment of Geographic Information System (GIS) analysis tools in CDWM is a powerful strategy for site suitability study. A case study in Coimbatore, India, is presented in this study using GIS-based multi-criteria analysis for locating C&D waste TS. The criteria for site suitability analysis are chosen based on literature review, regulations, and experts' opinions. Weights of the chosen criteria are estimated using analytic hierarchy process (AHP), and the final suitability map is created by weighted overlay analysis (WOA) in GIS environment. Results provide first-hand information for local decision makers to locate C&D waste transfer station in the chosen study region and report that 12% of the entire area is "highly suitable" for transfer station location.

India and ‘European’ evolutionary political economy

Evolutionary political economy (EPE) deals with populations and economic change over time but has not been systematised beyond European industrial history. The world’s largest democracy India, despite challenges from COVID-19, is now the fastest growing and 5th largest economy in the world, transitioning into a distinct period of industrial deepening, with export expansion in engineering products and services, huge outlays on airports, highways and rail systems, new domestic defence initiatives to boost indigenous R&D, yet retains a global diplomatic stance as a responsible nation–state from vaccine sharing to Asian security. This commentary explores the Indian case to argue that EPE’s combinatorial approach to technological capabilities and industrial development can be considerably strengthened from the study of non-European cases.

Criteria Selection of Housing Loan Based on Dominance-Based Rough Set Theory: An Indian Case

Because India has one of the world’s fastest-growing economies, the Indian banking sector is essential to the country’s reform. The approval of home loans to customers is one of the crucial tasks carried out by Indian banks. The risk of loan repayment outside of the agreed-upon time frame can be reduced by accurately estimating the customer’s loan need. The majority of earlier studies on the development of banking lacked a methodical approach to analyze qualitative data. Even though the traditional multivariate statistically based factor analysis approach is a great way to categories data in qualitative analysis, the technique cannot be used without any statistical presumptions and additional information about the data. This study handles the banking attributes related to home loans using the Dominance-based Rough Set Approach (D-RSA). In order to categorize the customer’s attributes, this study suggests using a preference-based “if … then” decision rule. This rule can aid decision makers in understanding the risk factors associated with loan factors for a financial organization.

How Much Should the Polluter Pay? Indian Courts and the Valuation of Environmental Damage

Abstract On the face of it, the polluter pays principle (PPP) simply prescribes that the costs of pollution should be borne by those who were responsible for causing it. In practice, implementing the PPP raises a number of complex questions. In this article, I focus on one such key question, as interpreted by higher courts in India: how much should the polluter pay? I propose—and then apply—a three-part choice framework for analysing judicial interpretations of the PPP. Indian case law on the subject is often presumed to be relatively coherent and consistent. However, a methodical application of the aforementioned framework reveals three distinct strands in the approaches taken by Indian courts, thus contributing to a more in-depth and systematic understanding of the PPP jurisprudence. I also identify certain gaps and inconsistencies in these approaches, and suggest ways in which they can be resolved, making the application of the set principle more consistent, logical and effective.

Exploring the Factors That Influence the Work–Family Interface of Construction Professionals: An Indian Case Study

The objective of this paper is to explore the dynamic factors of the work–family interface (WFI) of construction professionals in South India. It also aims to develop a model of the factors that influence the WFI. This study identified seven factors from the literature: work environment, family, stress, personal satisfaction, work culture, top-level acceptance, and work–family balance. Data were collected using questionnaires distributed among the construction professionals in South India. Data analysis was performed using SPSS. The mean standard deviation and shape measures (skewness and kurtosis) revealed that personal satisfaction (3.55) was rated the top factor for WFI, followed by work–family balance (3.15), stress (2.91), and work culture (2.83). The study found that the work culture was not significantly correlated with work environment, family, stress, personal satisfaction, or top-level acceptance. The developed SEM model emphasises the need to pay keen attention to the work environment and the work–family balance among the construction professionals in India. Management should consider these factors to design standard policies to improve the WFI and design work–life balance strategies to create stability in the lives of construction professionals. The current research is limited to only two cities in Kerala and Tamil Nadu in South India. More studies must be carried out for more states in India to better understand the current situation of WFI as there is limited evidence of studies on the WFI of construction professionals in India. Therefore, the findings of this study fill the existing knowledge gap and provide a clear insight into improving the mental and social well-being of construction professionals in the Indian construction industry and construction professionals in other developing countries.