The correctness of predictions rendered by an AI/ML model is key to its acceptability. To foster researchers' and practitioners' confidence in the model, it is necessary to render an intuitive understanding of the workings of a model. In this work, we attempt to explain a model's working by providing some insights into the quality of data. While doing this, it is essential to consider that revealing the training data to the users is not feasible for logistical and security reasons. However, sharing some interpretable parameters of the training data and correlating them with the model's performance can be helpful in this regard. To this end, we propose a new measure based on Euclidean Minimum Spanning Tree (EMST) for quantifying the intrinsic separation (or overlaps) between the data classes. For experiments, we use datasets from diverse domains such as finance, medical, and marketing. We use state-of-the-art measure known as Davies Bouldin Index (DBI) to validate our approach on four different datasets from aforementioned domains. The experimental results of this study establish the viability of the proposed approach in explaining the working and efficiency of a classifier. Firstly, the proposed measure of class- overlap quantification has shown a better correlation with the classification performance as compared to DBI scores. Secondly, the results on multi-class datasets demonstrate that the proposed measure can be used to determine the feature importance so as to learn a better classification model.

We introduce a linear programming-based approach for hyperparameter tuning of machine learning models. The approach finetunes continuous hyperparameters and model parameters through a linear program, enhancing model generalization in the vicinity of an initial model. The proposed method converts hyperparameter optimization into a bilevel program and identifies a descent direction to improve validation loss. The results demonstrate improvements in most cases across regression, machine learning, and deep learning tasks, with test performance enhancements ranging from 0.3% to 28.1%.

Income risks are not accurately captured by standard AR processes that are common in the literature. This paper proposes a simple stochastic process which matches several moments in the data including the cross-sectional distribution of income and the distribution income risk, and can be easily used in models with uninsurable income risk. Incorporating this process into an off-the-shelf OLG model leads to a rise in wealth concentration narrowing the gap between traditional models and the data. However, the right tail of the wealth distribution remains significantly thinner than the data.

Introduction: Clinical trials are critical for drug development and patient care; however, they often need more efficient trial design and patient enrolment processes. This research explores integrating machine learning (ML) techniques to address these challenges. Specifically, the study investigates ML models for two critical aspects: (1) streamlining clinical trial design parameters (like the site of drug action, type of Interventional/Observational model, etc) and (2) optimizing patient/volunteer enrolment for trials through efficient classification techniques. Methods: The study utilized two datasets: the first, with 55,000 samples (from ClinicalTrials.gov), was divided into five subsets (10,000-15,000 rows each) for model evaluation, focusing on trial parameter optimization. The second dataset targeted patient eligibility classification (from the UCI ML Repository). Five ML models-XGBoost, Random Forest, Support Vector Classifier (SVC), Logistic Regression, and Decision Tree-were applied to both datasets, alongside Artificial Neural Networks (ANN) for the second dataset. Model performance was evaluated using precision, recall, balanced accuracy, ROC-AUC, and weighted F1-score, with results averaged across k-fold cross-validation. Results: In the first phase, XGBoost and Random Forest emerged as the best-performing models across all five subsets, achieving an average balanced accuracy of 0.71 and an average ROC-AUC of 0.7. The second dataset analysis revealed that while SVC and ANN performed well, ANN was preferred for its scalability to larger datasets. ANN achieved a test accuracy of 0.73714, demonstrating its potential for real-world implementation in patient streamlining. Discussion: The study highlights the effectiveness of ML models in improving clinical trial workflows. XGBoost and Random Forest demonstrated robust performance for large clinical datasets in optimizing trial parameters, while ANN proved advantageous for patient eligibility classification due to its scalability. These findings underscore the potential of ML to enhance decision-making, reduce delays, and improve the accuracy of clinical trial outcomes. As ML technology continues to evolve, its integration into clinical research could drive innovation and improve patient care.

Waste minimisation and prevention are crucial for the circular economy (CE), sustainable development goals (SDGs) and environmental, social and governance (ESG) principles, focusing on waste elimination and resource efficiency. However, there are significant gaps in implementing effective waste minimisation strategies, mainly due to the lack of standardised waste prevention terminologies and indicators. This article introduces a novel simplified and comprehensive framework for assessing waste prevention and minimisation measures tailored to developing countries. The primary contribution of this study lies in proposing relevant indicators aligned with the SDGs, ESG standards, and CE principles, while addressing data scarcity through proxy indicators to enable effective assessment in resource-limited settings. Six key indicators were proposed: Zero Waste Index, Food Loss Index, Extended producer responsibility, Education and awareness programmes for waste minimisation, Waste prevention and Plastic Bag Reduction Ratio. Eleven countries were selected as case studies to demonstrate the framework's applicability. The findings reveal that while these countries are progressing in enacting legislation and recognising the importance of waste prevention, compliance in practice is lacking, as indicated by poor quantitative results in actual waste reduction and diversion. The framework evaluates the environmental, social and economic implications of waste prevention measures, showing wide variations among countries. Each country faces unique challenges, but strengthening policy frameworks, investing in infrastructure, promoting public awareness and fostering collaboration are key steps towards advancing sustainable waste management practices. The study highlights the necessity for tailored policies addressing specific weaknesses while ensuring economic viability. The integrated framework provides actionable insights and forward-thinking solutions that can be adapted, scaled and replicated to address developing nations' unique challenges.

This paper reports a study of how the Indian IT industry navigated the COVID-19 pandemic.Agility emerged as the crucial determining factor aiding the industry's successful survival. IT orga-nisations'agility, facilitated by the state's response to the pandemic and by both their anticipation ofthe lockdown and their technological capabilities, helped them overcome the crisis. The slowdownforced firms to downsize, reduce bench strength, freeze wages and intensify work, while deferringclient payments. When the economy recovered, high attrition, termed'The Great Resignation', forcedemployers to increase wages. Employers were unable to compel employees to return to the office,despite facing issues relating to organisational culture, data security and moonlighting. Remote work-ing helped employees maintain work-life balance and save on cost of living, forcing employers toprovide a hybrid option.

Gold plays an important role as a hedge and a safe haven for investors. This paper presents new evidence on gold's role for Indian households during the COVID-19 pandemic. By using panel and cross-sectional household surveys, we investigate the propensity of households to purchase, pledge or sell gold based on the district-level heterogeneity in COVID cases and economic impact proxied by night-time light activity. We find higher gold purchases of households in the more affected districts compared to other districts during the crisis. Importantly, households that are more directly affected by the shock are less likely to purchase gold and more likely to pledge or sell gold. The purchases are likely driven by an increased risk perception of households in response to an unexpected shock - a novel perspective of gold's safe haven property. We also find that relatively poor households that receive government transfers or have less access to formal credit display stronger gold purchasing behavior.

'Mindful consumption' in tourism services helps to create a positive experience for the consumer by enabling the latter to effectively utilise the resources available at the tourist destination. While numerous studies have explored the idea of mindful consumption, most of them have largely examined the overarching concepts. In contrast, this study focuses on evaluating the effect of mindful consumption in the tourism sector on the residents' well-being of the tourist destination using Stimulus-Organism-Response (SOR) Theory. A mixed method was used to evaluate the impact of the antecedents of mindful consumption on residents' well-being of the tourist destination. A qualitative and quantitative study was also conducted to test the proposed conceptual model. The study extends the SOR Theory with new and novel constructs in the context of tourism. It also lists managerial implications for travel agents and providers of tourism services to attract consumers endowed with a high environmentally sustainable attitude and self-consciousness.

We model the behaviour of a profit-maximising producer in a region where carbon prices may be uncertain (possibly due to implementing emissions trading system (ETS)) or known deterministically (possibly due to carbon tax). In particular, we analyse the propensity of the producer to shift a part of their operations offshore (carbon leakage) to avoid paying for emissions. Using a two-stage stochastic optimisation model, we show striking difference in a producer's long-term investment decisions in the presence and absence of carbon price uncertainty. When the producer knows the carbon price deterministically, she either invests in converting existing domestic infrastructure to more sustainable ones or installs new capacity offshore, depending upon the magnitude of the carbon price, but never both. However, when the carbon price is uncertain, the producer could gain by simultaneously investing in domestic upgrades as well as an offshore plant, indicating guaranteed carbon leakage. While the behaviours could be different, we show that a carefully designed import duty could combat carbon leakage effectively, irrespective of the uncertainties. This, further indicates that, when used in combination with an import duty as a policy instrument, both carbon tax as well as ETS act as effective environmental policies.

Market access for smallholders is a key policy issue in developing economies. However, smallholders often face barriers in accessing remunerative marketing channels. Combining the theoretical framework drawn from resource-based view, social exchange, and transaction cost theory, we jointly estimate the determinants of marketing channel choice and the impact of channel choice in a joint framework. Results show that households' resource endowments and social capital influence channel selection decision. Paddy sales through government agencies help farmers realize higher prices because of the higher government support prices and proximity to farms, whereas sales through licensed traders operating in the regulated markets (APMC) results in reduced paddy prices due to high transportation costs. Furthermore, smallholders preferring government agencies, processors, and licensed traders over village traders realize greater farm income. Reasons for such findings can be deduced to be the result of low marketable surplus for smallholders selling to village traders.

In this article, I move beyond the traditional outlook on democracy constructed around freedom and equality by bringing a new lens of belongingness. After establishing belongingness as an essential democratic ethos by engaging with existing scholarship, I look at the case study of India around the question of belongingness of the minority Muslim population in Hindutva India. I specifically explore the case of the Anti-CAA-NRC protest as a movement of asserting belongingness and how the solidarity from members of different communities and groups helped reassert the belongingness of the Indian Muslim citizens against the Hindutva state.

India is a leading global hot spot for extreme heat waves induced by climate change. The social demography of India is centered on its caste hierarchy rooted in endogamous occupational groups. We investigate the association between caste and climate inequality by studying occupational exposure during the 2019 and 2022 heat waves. We combine high spatiotemporal resolution heat stress information from satellite imagery with a large nationally and regionally representative labor force survey with rich socioeconomic and demographic information (n > 100,000 individuals). The slope of the heat stress dose-workhours curve corresponding to the marginalized caste groups is between 25% and 150% steeper than that for dominant caste groups for UTCI (Universal Thermal Climate Index) thresholds between 26 degrees C and 35 degrees C. Our models control for other economic-demographic confounders, including age, gender, education, and economic status, besides political-geographic controls and fixed effects. Our robust evidence for the association between caste identity and exposure to heat stress shows why adaptation and mitigation plans in India must account for the hierarchical social order characterized by the division of laborers along caste lines rather than the mere division of labor. Methodologically, our analysis demonstrates the utility of pairing satellite imagery and detailed demographic data.

This paper focuses on secondary creep analysis in functionally graded (FG) rotating cylinders subjected to internal and external pressure. For a prolonged period of time, when the material undergoes a steady magnitude of load, it experiences a time-dependent deformation that is irreversible and known as a creep deformation. This paper analyses secondary creep stresses and strains in functionally graded cylinders with different volume reinforcement profiles and different material combinations. An anisotropic cylinder with varying nonlinear volume reinforcement of the ceramic in the metal matrix is considered and closed form results are obtained for the creep stresses and creep strains in the radial and tangential directions. The results are plotted graphically, and the implications of the results are discussed using the numerical values over the radial dimension of the functionally graded cylinder.

Amplification of informational social media posts are crucial for disseminating tobacco control messages as part of an integrated behavior change strategy. Our study explores the amplification effectiveness of fear-based, trust-based, and linguistically complex framing for tobacco control appeals from credible sources and in general. Analyzing 42,261 tweets from 102 accounts using natural language processing techniques, we found fear-based (38.75%) and complex appeals (31.53%) increased retweets, while trust-based decreased them (-37.28%). Credible sources amplified trust-based (58.98%) and fear-based (69.58%) appeals but reduced the amplification of complex messages (-11.01%). Consequently, we highlight the need for strategic framing of tobacco control messages for greater impact.

This study addresses a new problem, close to real life on scheduling of non-identical parallel Burn-in ovens (BO) in Semiconductor Manufacturing (SM) industry to minimize Total Weighted Tardiness (TWT) of the jobs. Due to computational intractability in using mathematical programming approach, many researchers considered Dispatching Rules (DR) based heuristic algorithm in SM industry for scheduling of various batch processing machines, including BO. However, there is no study that compares the various DR considered for scheduling of BO in general, particularly in (a) Dynamic Scheduling (DS) of Non-identical Parallel Burn-in Ovens (NPBO) with Machine Eligibility Restriction (MER) and (b) Real-Time DS (RTDS) of NPBO with MER. To address this research gap, this study proposed 25 variants of DR based Heuristic Algorithm (DR-HA). Empirical and statistical performance analyses, carried out with 240 test data, revealed that variants of DR-HA that outperform during DS-NPBO with MER also outperform in RTDS-NPBO with MER. Furthermore, this study gives an important inference that whenever any RTE occurs in RTDS-NPBO, there is no need to modify existing efficient algorithm or no need to develop new algorithm for RTDS-NPBO and updating input data related to type of occurred RTE is sufficient before running existing efficient DR-HA for DS-NPBO.

Biodegradable polymers are substitutes for non-biodegradable polymers manufactured from fossil fuels to reduce plastic waste. Polylactic acid is a promising candidate for biodegradable polymers. Antimicrobial agents such as silver and copper are added to polylactic acid to improve its antimicrobial properties for food packaging applications. In this work, silver nanoparticles were infiltrated into the polylactic acid matrix to form composite films. The presence of silver nanoparticles has enhanced antimicrobial resistance with attested its suitability to be explored like food packaging film. Additionally, the nanoparticle composite film was to be non-toxic and to have high durability. The properties of polylactic acid/silver nanoparticle films were studied as a function of the silver nanoparticle's weight percentage (0%, 5% and 10%). The incorporation of silver nanoparticles in the polylactic acid matrix was established by Fourier transform infrared spectroscopy spectrometer, ultraviolet-visible spectrometer, and field emission-scanning electron microscopy. Fourier transform infrared spectroscopy and ultraviolet-visible spectrum suggest the presence of silver nanoparticles in the composite films. Thermal gravimetric analysis and differential calorimeter analysis reveal the change in degradation and glass transition temperature with an increase in silver nanoparticle concentration. The antibacterial property using the disk diffusion method indicates that polylactic acid/silver nanoparticles (10%) had the highest antibacterial property. Antibacterial action is enhanced when silver nanoparticles are combined with a tiny dose of antibiotics, especially when battling multi-strain-resistant bacteria.

Digital technologies (DTs) are increasingly recognized as crucial in addressing social issues related to inequality and enhancing the well-being and agency of socially marginalized groups. We however, provide evidence that, instead of alleviating social inequalities, use of DTs (re) produced and exacerbated these inequalities in disparate forms, for an already marginalized population. Based on a qualitative study of employees from five microfinance institutions (MFIs) in India that offer uncollateralized group loans to poor rural women, our findings demonstrate how the pursuit of financial gains through DTs in providing microfinance exacerbated mission drift in MFIs, leading to reduced quality and depth of outreach. The use of DTs undermined social and human capital development both crucial for alleviating poverty and widened exclusion rather than bridging the gap. We explicate the quality of outreach (i.e., quality of services provided) as an additional dimension of social outreach, alongside the depth of outreach (i.e., reaching poorer borrowers) for understanding mission drift. Our findings call for consideration of existing intersectional social inequalities when leveraging DTs for social causes.

PurposeResearchers have become increasingly interested in the agglomeration of firms into industry clusters and the effects of such clusters on firms. We analyze the effects of exposure to original equipment manufacturers (OEMs) and industry clusters on supplier performance, focusing on the avoidance of operations disruptions and the support of a smooth production ramp-up at the OEM, by assessing the suppliers of a multinational automotive OEM in India. We study how the exposure of suppliers to the focal OEM and to clusters affects the ability of suppliers to continuously provide their parts to the OEM within pre-agreed schedules and specifications.Design/methodology/approachWe use regression based on performance data of 162 domestic suppliers of a multinational OEM.FindingsOur findings suggest that clustered firms demonstrate superior performance by leveraging increased exposure to other firms within the cluster. Also, through increased exposure to the OEM, both clustered and non-clustered suppliers improve their performance, but at different rates depending on their prior cluster exposure. Our results provide evidence that OEM exposure and prior cluster exposure act as substitutes for suppliers located in clusters, enabling these suppliers to perform better, especially in the initial phases of a supply relationship.Originality/valueThis article fills a gap in the research on cluster implementation in developing economies by studying the impact of location in industry clusters on supplier performance and supplier development.

Robotic sorting systems (RSSs) use mobile robots to sort items by destination. An RSS pairs high accuracy and flexible capacity sorting with the advantages of a flexible layout. This is why several express parcel and e-commerce retail companies, who face heavy demand fluctuations, have implemented these systems. To cope with fluctuating demand, temporal robot congestion, and high sorting speed requirements, workload balancing strategies such as dynamic robot routing and destination reassignment may be of benefit. We investigate the effect of a dynamic robot routing policy using a Markov decision process (MDP) model and dynamic destination assignment using a mixed integer programming (MIP) model. To obtain the MDP model parameters, we first model the system as a semiopen queuing network (SOQN) that accounts for robot movement dynamics and network congestion. Then, we construct the MIP model to find a destination reassignment scheme that minimizes the workload imbalance. With inputs from the SOQN and MIP models, the Markov decision process minimizes parcel waiting and postponement costs and helps to find a good heuristic robot routing policy to reduce congestion. We show that the heuristic dynamic routing policy is near optimal in small-scale systems and outperforms benchmark policies in large-scale realistic scenarios. Dynamic destination reassignment also has positive effects on the throughput capacity in highly loaded systems. Together, in our case company, they improve the throughput capacity by 35%. Simultaneously, the effect of dynamic routing exceeds that of dynamic destination reassignment, suggesting that managers should focus more on dynamic robot routing than dynamic destination reassignment to mitigate temporal congestion.

PurposeWhat are the effects of household characteristics on returns for entrepreneurs in low-income settings? Referred to as subsistence consumer-merchants or consumer-entrepreneurs in the marketing literature and necessity entrepreneurs in the entrepreneurship literature, such entrepreneurs run family businesses to survive and make ends meet. This paper aims to investigate how various household-level factors (education), individual-level factors (education and gender) and the type of enterprise (retail vs manufacturing) impact returns from low-income family businesses compared to high-income family businesses.Design/methodology/approachUsing panel data from the India Human Development Surveys (IHDS), the study uses a fixed-effects regression approach to analyze household-level and individual-level factors on business performance and enterprise failure.FindingsEducation, particularly at the household level, has a positive impact on enterprise income and reduces the likelihood of enterprise failure, with stronger effects observed for lower-income entrepreneurs. Gender disparities persist, with male-led enterprises generating higher income overall but women-led enterprises in low-income settings showing lower failure rates. Family involvement enhances entrepreneurial outcomes but presents unique risks for lower-income entrepreneurs. No significant differences were found between retail and manufacturing enterprises in terms of income or failure rates.Originality/valueThe research bridges the disciplines of entrepreneurship and marketing by highlighting the interplay of household characteristics in driving necessity entrepreneurship in low-income settings. This paper underscores the dual roles of family and education in shaping entrepreneurial success and failure, offering nuanced and novel insights for theory and practice in emerging economies.